annotate writeup/mlj_submission/aigaion-shorter.bib @ 645:9455871d4703

pdf du poster
author Yoshua Bengio <bengioy@iro.umontreal.ca>
date Thu, 07 Apr 2011 13:32:39 -0400
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rev   line source
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1 %Aigaion2 BibTeX export from LISA - Publications
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2 %Tuesday 01 June 2010 10:46:52 AM
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3 @INPROCEEDINGS{Attardi+al-2009,
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4 author = {Attardi, Giuseppe and Dell'Orletta, Felice and Simi, Maria and Turian, Joseph},
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5 keywords = {classifier, dependency parsing, natural language, parser, perceptron},
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6 title = {Accurate Dependency Parsing with a Stacked Multilayer Perceptron},
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7 booktitle = {Proceeding of Evalita 2009},
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8 series = {LNCS},
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9 year = {2009},
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10 publisher = {Springer},
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11 abstract = {Abstract. DeSR is a statistical transition-based dependency parser which learns from annotated corpora which actions to perform for building parse trees while scanning a sentence. We describe recent improvements to the parser, in particular stacked parsing, exploiting a beam search strategy and using a Multilayer Perceptron classifier. For the Evalita 2009 Dependency Parsing task DesR was configured to use a combination of stacked parsers. The stacked combination achieved the best accuracy scores in both the main and pilot subtasks. The contribution to the result of various choices is analyzed, in particular for taking advantage of the peculiar features of the TUT Treebank.}
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12 }
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13
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14 @INPROCEEDINGS{Bengio+al-2009,
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15 author = {Bengio, Yoshua and Louradour, Jerome and Collobert, Ronan and Weston, Jason},
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16 title = {Curriculum Learning},
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17 year = {2009},
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18 crossref = {ICML09-shorter},
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19 abstract = {Humans and animals learn much better when the examples are not randomly presented but organized in a meaningful order which illustrates gradually more concepts, and more complex ones. Here, we formalize such training strategies in the context of machine learning, and call them 'curriculum learning'. In the context of recent research studying the difficulty of training in the presence of non-convex training criteria (for deep deterministic and stochastic neural networks), we explore curriculum learning in various set-ups. The experiments show that significant improvements in generalization can be achieved by using a particular curriculum, i.e., the selection and order of training examples. We hypothesize that curriculum learning has both an effect on the speed of convergence of the training process to a minimum and, in the case of non-convex criteria, on the quality of the local minima obtained: curriculum learning can be seen as a particular form of continuation method (a general strategy for global optimization of non-convex functions).}
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20 }
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21
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22 @TECHREPORT{Bengio+al-2009-TR,
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23 author = {Bengio, Yoshua and Louradour, Jerome and Collobert, Ronan and Weston, Jason},
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24 title = {Curriculum Learning},
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25 number = {1330},
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26 year = {2009},
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27 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
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28 abstract = {Humans and animals learn much better when the examples are not randomly presented but organized in a meaningful order which illustrates gradually more concepts, and gradually more complex ones. Here, we formalize such training strategies in the context of machine learning, and call them 'curriculum learning'. In the context of recent research studying the difficulty of training in the presence of non-convex training criteria (for deep deterministic and stochastic neural networks), we explore curriculum learning in various set-ups. The experiments show that significant improvements in generalization can be achieved. We hypothesize that curriculum learning has both an effect on the speed of convergence of the training process to a minimum and, in the case of non-convex criteria, on the quality of the local minima obtained: curriculum learning can be seen as a particular form of continuation method (a general strategy for global optimization of non-convex functions).}
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29 }
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30
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31 @MISC{Bengio+al-patent-2000,
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32 author = {Bengio, Yoshua and Bottou, {L{\'{e}}on} and {LeCun}, Yann},
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33 title = {Module for constructing trainable modular network in which each module outputs and inputs data structured as a graph},
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34 year = {2000},
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35 howpublished = {U.S. Patent 6,128,606, October 3}
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36 }
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37
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38 @MISC{Bengio+al-patent-2001,
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39 author = {Bengio, Yoshua and Bottou, {L{\'{e}}on} and G. Howard, Paul},
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40 title = {Z-Coder : a fast adaptive binary arithmetic coder},
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41 year = {2001},
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42 howpublished = {U.S. Patent 6,188,334, February 13, 2001, along with patents 6,225,925, 6,281,817, and 6,476,740}
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43 }
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44
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45 @MISC{Bengio+al-patent-94,
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46 author = {Bengio, Yoshua and {LeCun}, Yann and Nohl, Craig and Burges, Chris},
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47 title = {Visitor Registration System Using Automatic Handwriting Recognition},
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48 year = {1994},
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49 howpublished = {Patent submitted in the U.S.A. in October 1994, submission number 1-16-18-1}
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50 }
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51
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52 @INCOLLECTION{Bengio+al-spectral-2006,
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53 author = {Bengio, Yoshua and Delalleau, Olivier and Le Roux, Nicolas and Paiement, Jean-Fran{\c c}ois and Vincent, Pascal and Ouimet, Marie},
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54 editor = {Guyon, Isabelle and Gunn, Steve and Nikravesh, Masoud and Zadeh, Lofti},
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55 title = {Spectral Dimensionality Reduction},
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56 booktitle = {Feature Extraction, Foundations and Applications},
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57 year = {2006},
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58 publisher = {Springer},
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59 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/eigenfn_chapter.pdf},
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60 abstract = {In this chapter, we study and put under a common framework a number
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61 of non-linear dimensionality reduction methods, such as Locally Linear Embedding,
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62 Isomap, Laplacian eigenmaps and kernel {PCA}, which are based
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63 on performing an eigen-decomposition (hence the name "spectral"). That
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64 framework also includes classical methods such as {PCA} and metric multidimensional
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65 scaling ({MDS}). It also includes the data transformation step used
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66 in spectral clustering. We show that in all of these cases the learning algorithm
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67 estimates the principal eigenfunctions of an operator that depends on
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68 the unknown data density and on a kernel that is not necessarily positive
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69 semi-definite. This helps to generalize some of these algorithms so as to predict
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70 an embedding for out-of-sample examples without having to retrain the
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71 model. It also makes it more transparent what these algorithm are minimizing
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72 on the empirical data and gives a corresponding notion of generalization
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73 error.},
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74 cat={B},topics={HighDimensional,Kernel,Unsupervised},
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75 }
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76
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77 @INCOLLECTION{Bengio+al-ssl-2006,
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78 author = {Bengio, Yoshua and Delalleau, Olivier and Le Roux, Nicolas},
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79 editor = {Chapelle, Olivier and {Sch{\"{o}}lkopf}, Bernhard and Zien, Alexander},
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80 title = {Label Propagation and Quadratic Criterion},
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81 booktitle = {Semi-Supervised Learning},
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82 year = {2006},
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83 pages = {193--216},
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84 publisher = {{MIT} Press},
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85 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/bengio_ssl.pdf},
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86 abstract = {Various graph-based algorithms for semi-supervised learning have been proposed in
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87 the recent literature. They rely on the idea of building a graph whose nodes are
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88 data points (labeled and unlabeled) and edges represent similarities between points.
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89 Known labels are used to propagate information through the graph in order to label
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90 all nodes. In this chapter, we show how these different algorithms can be cast into
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91 a common framework where one minimizes a quadratic cost criterion whose closed-form solution is found by solving a linear system of size n (total number of data
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92 points). The cost criterion naturally leads to an extension of such algorithms to
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93 the inductive setting, where one obtains test samples one at a time: the derived
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94 induction formula can be evaluated in O(n) time, which is much more efficient
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95 than solving again exactly the linear system (which in general costs O(kn2) time
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96 for a sparse graph where each data point has k neighbors). We also use this inductive
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97 formula to show that when the similarity between points satisfies a locality property,
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98 then the algorithms are plagued by the curse of dimensionality, with respect to the
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99 dimensionality of an underlying manifold.},
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100 cat={B},topics={Unsupervised},
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101 }
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102
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103 @TECHREPORT{Bengio+al-treecurse-2007,
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104 author = {Bengio, Yoshua and Delalleau, Olivier and Simard, Clarence},
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105 title = {Decision Trees do not Generalize to New Variations},
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106 number = {1304},
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107 year = {2007},
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108 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
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109 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/bengio+al-tr1304.pdf}
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110 }
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111
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112 @INPROCEEDINGS{Bengio+Bengio96,
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113 author = {Bengio, Samy and Bengio, Yoshua},
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114 editor = {Xu, L.},
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115 title = {An {EM} Algorithm for Asynchronous Input/Output Hidden {M}arkov Models},
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116 booktitle = {International Conference On Neural Information Processing},
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117 year = {1996},
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118 pages = {328--334},
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119 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/iconip96.pdf},
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120 abstract = {In learning tasks in which input sequences are mapped to output sequences, it is often the case that the input and output sequences are not synchronous. For example, in speech recognition, acoustic sequences are longer than phoneme sequences. Input/Output Hidden {Markov} Models have already been proposed to represent the distribution of an output sequence given an input sequence of the same length. We extend here this model to the case of asynchronous sequences_ and show an Expectation-Maximization algorithm for training such models.},
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121 topics={Markov},cat={C},
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122 }
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123
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124 @INCOLLECTION{Bengio+chapter2007,
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125 author = {Bengio, Yoshua and {LeCun}, Yann},
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126 editor = {Bottou, {L{\'{e}}on} and Chapelle, Olivier and DeCoste, D. and Weston, J.},
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127 title = {Scaling Learning Algorithms towards {AI}},
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128 booktitle = {Large Scale Kernel Machines},
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129 year = {2007},
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130 publisher = {MIT Press},
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131 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/bengio+lecun_chapter2007.pdf},
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132 abstract = {One long-term goal of machine learning research is to produce methods that
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133 are applicable to highly complex tasks, such as perception (vision, audition), reasoning,
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134 intelligent control, and other artificially intelligent behaviors. We argue
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135 that in order to progress toward this goal, the Machine Learning community must
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136 endeavor to discover algorithms that can learn highly complex functions, with minimal
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137 need for prior knowledge, and with minimal human intervention. We present
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138 mathematical and empirical evidence suggesting that many popular approaches
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139 to non-parametric learning, particularly kernel methods, are fundamentally limited
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140 in their ability to learn complex high-dimensional functions. Our analysis
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141 focuses on two problems. First, kernel machines are shallow architectures, in
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142 which one large layer of simple template matchers is followed by a single layer
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143 of trainable coefficients. We argue that shallow architectures can be very inefficient
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144 in terms of required number of computational elements and examples. Second,
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145 we analyze a limitation of kernel machines with a local kernel, linked to the
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146 curse of dimensionality, that applies to supervised, unsupervised (manifold learning)
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147 and semi-supervised kernel machines. Using empirical results on invariant
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148 image recognition tasks, kernel methods are compared with deep architectures, in
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149 which lower-level features or concepts are progressively combined into more abstract
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150 and higher-level representations. We argue that deep architectures have the
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151 potential to generalize in non-local ways, i.e., beyond immediate neighbors, and
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152 that this is crucial in order to make progress on the kind of complex tasks required
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153 for artificial intelligence.},
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154 cat={B},topics={HighDimensional},
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155 }
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156
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157 @ARTICLE{Bengio+Delalleau-2009,
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158 author = {Bengio, Yoshua and Delalleau, Olivier},
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159 title = {Justifying and Generalizing Contrastive Divergence},
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160 journal = {Neural Computation},
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161 volume = {21},
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162 number = {6},
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163 year = {2009},
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164 pages = {1601--1621},
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165 abstract = {We study an expansion of the log-likelihood in undirected graphical models such as the Restricted {Boltzmann} Machine (RBM), where each term in the expansion is associated with a sample in a Gibbs chain alternating between two random variables (the visible vector and the hidden vector, in RBMs). We are particularly interested in estimators of the gradient of the log-likelihood obtained through this expansion. We show that its residual term converges to zero, justifying the use of a truncation, i.e. running only a short Gibbs chain, which is the main idea behind the Contrastive Divergence (CD) estimator of the log-likelihood gradient. By truncating even more, we obtain a stochastic reconstruction error, related through a mean-field approximation to the reconstruction error often used to train autoassociators and stacked auto-associators. The derivation is not specific to the particular parametric forms used in RBMs, and only requires convergence of the Gibbs chain. We present theoretical and empirical evidence linking the number of Gibbs steps $k$ and the magnitude of the RBM parameters to the bias in the CD estimator. These experiments also suggest that the sign of the CD estimator is correct most of the time, even when the bias is large, so that CD-$k$ is a good descent direction even for small $k$.}
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166 }
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167
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168 @TECHREPORT{Bengio+Delalleau-TR2007,
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169 author = {Bengio, Yoshua and Delalleau, Olivier},
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170 keywords = {Contrastive Divergence, Restricted {Boltzmann} Machine},
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171 title = {Justifying and Generalizing Contrastive Divergence},
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172 number = {1311},
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173 year = {2007},
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174 institution = {D{\'{e}}partement d'Informatique et Recherche Op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
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175 abstract = {We study an expansion of the log-likelihood in undirected graphical models such as the Restricted {Boltzmann} Machine (RBM), where each term in the expansion is associated with a sample in a Gibbs chain alternating between two random variables (the visible vector and the hidden vector, in RBMs). We are particularly interested in estimators of the gradient of the log-likelihood obtained through this expansion. We show that its terms converge to zero, justifying the use of a truncation, i.e. running only a short Gibbs chain, which is the main idea behind the Contrastive Divergence approximation of the log-likelihood gradient. By truncating even more, we obtain a stochastic reconstruction error, related through a mean-field approximation to the reconstruction error often used to train autoassociators and stacked auto-associators. The derivation is not specific to the particular parametric forms used in RBMs, and only requires convergence of the Gibbs chain.}
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176 }
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177
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178 @INPROCEEDINGS{Bengio+DeMori88,
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179 author = {Bengio, Yoshua and De Mori, Renato},
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180 title = {Use of neural networks for the recognition of place of articulation},
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181 booktitle = {International Conference on Acoustics, Speech and Signal Processing},
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182 year = {1988},
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183 pages = {103--106},
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184 topics={Speech},cat={C},
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185 }
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186
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187 @INPROCEEDINGS{Bengio+DeMori89,
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188 author = {Bengio, Yoshua and Cardin, Regis and Cosi, Piero and De Mori, Renato},
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189 title = {Speech coding with multi-layer networks},
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190 booktitle = {International Conference on Acoustics, Speech and Signal Processing},
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191 year = {1989},
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192 pages = {164--167},
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193 topics={Speech},cat={C},
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194 }
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195
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196 @INCOLLECTION{Bengio+DeMori90a,
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197 author = {Bengio, Yoshua and De Mori, Renato},
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198 editor = {Sethi, I. K. and Jain, A. K.},
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199 title = {Connectionist models and their application to automatic speech recognition},
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200 booktitle = {Artificial Neural Networks and Statistical Pattern Recognition: Old and New Connections},
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201 year = {1990},
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202 pages = {175--192},
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203 publisher = {Elsevier, Machine Intelligence and Pattern Recognition Series},
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204 topics={Speech},cat={B},
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205 }
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206
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207 @ARTICLE{Bengio+Frasconi-jair95,
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208 author = {Bengio, Yoshua and Frasconi, Paolo},
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209 title = {Diffusion of Context and Credit Information in {M}arkovian Models},
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210 journal = {Journal of Artificial Intelligence Research},
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211 volume = {3},
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212 year = {1995},
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213 pages = {249--270},
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214 abstract = {This paper studies the problem of ergodicity of transition probability matrices in {Markovian} models, such as hidden {Markov} models ({HMM}s), and how it makes very difficult the task of learning to represent long-term context for sequential data. This phenomenon hurts the forward propagation of long-term context information, as well as learning a hidden state representation to represent long-term context, which depends on propagating credit information backwards in time. Using results from {Markov} chain theory, we show that this problem of diffusion of context and credit is reduced when the transition probabilities approach 0 or 1, i.e., the transition probability matrices are sparse and the model essentially deterministic. The results found in this paper apply to learning approaches based on continuous optimization, such as gradient descent and the Baum-Welch algorithm.},
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215 topics={Markov,LongTerm},cat={J},
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216 }
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217
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218 @INPROCEEDINGS{Bengio+Frasconi-nips7-diffuse,
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219 author = {Bengio, Yoshua and Frasconi, Paolo},
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220 title = {Diffusion of Credit in {M}arkovian Models},
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221 year = {1995},
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222 pages = {553--560},
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223 crossref = {NIPS7-shorter},
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224 abstract = {This paper studies the problem of diffusion in {Markovian} models, such as hidden {Markov} models ({HMM}s) and how it makes very difficult the task of learning of long-term dependencies in sequences. Using results from {Markov} chain theory, we show that the problem of diffusion is reduced if the transition probabilities approach 0 or 1. Under this condition, standard {HMM}s have very limited modeling capabilities, but input/output {HMM}s can still perform interesting computations.},
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225 topics={Markov},cat={C},
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226 }
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227
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228 @INPROCEEDINGS{Bengio+Frasconi-nips7-iohmms,
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229 author = {Bengio, Yoshua and Frasconi, Paolo},
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230 title = {An Input/Output {HMM} Architecture},
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231 year = {1995},
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232 pages = {427--434},
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233 crossref = {NIPS7-shorter},
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234 abstract = {We introduce a recurrent architecture having a modular structure and we formulate a training procedure based on the {EM} algorithm. The resulting model has similarities to hidden {Markov} models, but supports recurrent networks processing style and allows to exploit the supervised learning paradigm while using maximum likelihood estimation.},
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235 topics={Markov},cat={C},
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236 }
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237
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238 @INPROCEEDINGS{Bengio+Frasconi-nips94,
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239 author = {Bengio, Yoshua and Frasconi, Paolo},
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240 title = {Credit Assignment through Time: Alternatives to Backpropagation},
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241 year = {1994},
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242 pages = {75--82},
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243 crossref = {NIPS6-shorter},
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244 abstract = {Learning to recognize or predict sequences using long-term context has many applications. However, practical and theoretical problems are found in training recurrent neural networks to perform tasks in which input/output dependencies span long intervals. Starting from a mathematical analysis of the problem, we consider and compare alternative algorithms and architectures on tasks for which the span of the input/output dependencies can be controlled. Results on the new algorithms show performance qualitatively superior to that obtained with backpropagation.},
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245 topics={LongTerm},cat={C},
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246 }
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247
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248 @ARTICLE{Bengio+Pouliot90,
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249 author = {Bengio, Yoshua and Pouliot, Yannick},
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250 title = {Efficient recognition of immunoglobulin domains from amino-acid sequences using a neural network},
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251 journal = {Computer Applications in the Biosciences},
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252 volume = {6},
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253 number = {2},
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254 year = {1990},
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255 pages = {319--324},
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256 topics={Bioinformatic,PriorKnowledge},cat={J},
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257 }
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258
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259 @INPROCEEDINGS{Bengio+Senecal-2003,
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260 author = {Bengio, Yoshua and S{\'{e}}n{\'{e}}cal, Jean-S{\'{e}}bastien},
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261 title = {Quick Training of Probabilistic Neural Nets by Importance Sampling},
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262 booktitle = {Proceedings of the conference on Artificial Intelligence and Statistics (AISTATS)},
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263 year = {2003},
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264 abstract = {Our previous work on statistical language modeling introduced the use of probabilistic feedforward neural networks to help dealing with the curse of dimensionality. Training this model by maximum likelihood however requires for each example to perform as many network passes as there are words in the vocabulary. Inspired by the contrastive divergence model, we propose and evaluate sampling-based methods which require network passes only for the observed "positive example'' and a few sampled negative example words. A very significant speed-up is obtained with an adaptive importance sampling.}
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265 }
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266
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267 @ARTICLE{Bengio+Senecal-2008,
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268 author = {Bengio, Yoshua and S{\'{e}}n{\'{e}}cal, Jean-S{\'{e}}bastien},
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269 keywords = {Energy-based models, fast training, importance sampling, language modeling, Monte Carlo methods, probabilistic neural networks},
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270 title = {Adaptive Importance Sampling to Accelerate Training of a Neural Probabilistic Language Model},
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271 journal = {IEEE Transactions on Neural Networks},
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272 volume = {19},
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273 number = {4},
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274 year = {2008},
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275 pages = {713--722},
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276 abstract = {Previous work on statistical language modeling has shown that it is possible to train a feedforward neural network to approximate probabilities over sequences of words, resulting in significant error reduction when compared to standard baseline models based on -grams. However, training the neural network model with the maximum-likelihood criterion requires computations proportional to the number of words in the vocabulary. In this paper, we introduce adaptive importance sampling as a way to accelerate training of the model. The idea is to use an adaptive n-gram model to track the conditional distributions produced by the neural network. We show that a very significant speedup can be obtained on standard problems.}
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277 }
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278
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279 @INCOLLECTION{Bengio-2007,
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280 author = {Bengio, Yoshua},
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281 editor = {Cisek, Paul and Kalaska, John and Drew, Trevor},
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282 title = {On the Challenge of Learning Complex Functions},
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283 booktitle = {Computational Neuroscience: Theoretical Insights into Brain Function},
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284 series = {Progress in Brain Research},
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285 year = {2007},
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286 publisher = {Elsevier},
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287 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/PBR_chapter.pdf},
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288 abstract = {A common goal of computational neuroscience and of artificial intelligence
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289 research based on statistical learning algorithms is the discovery and
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290 understanding of computational principles that could explain what we
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291 consider adaptive intelligence, in animals as well as in machines. This
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292 chapter focuses on what is required for the learning of complex behaviors. We
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293 believe it involves the learning of highly varying functions, in a
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294 mathematical sense. We bring forward two types of arguments which convey
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295 the message that many currently popular machine learning approaches to
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296 learning flexible functions have fundamental limitations that render them
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297 inappropriate for learning highly varying functions. The first issue
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298 concerns the representation of such functions with what we call shallow model
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299 architectures. We discuss limitations of shallow architectures, such as
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300 so-called kernel machines, boosting algorithms, and one-hidden-layer artificial neural
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301 networks. The second issue is more focused and concerns kernel machines
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302 with a local kernel (the type used most often in practice),
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303 that act like a collection of template matching units. We present
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304 mathematical results on such computational architectures showing that they
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305 have a limitation similar to those already proved for older non-parametric
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306 methods, and connected to the so-called curse of dimensionality. Though it has long
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307 been believed that efficient learning in deep architectures is difficult,
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308 recently proposed computational principles for learning in deep architectures
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309 may offer a breakthrough.}
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310 }
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311
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312 @ARTICLE{Bengio-2009,
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313 author = {Bengio, Yoshua},
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314 title = {Learning deep architectures for {AI}},
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315 journal = {Foundations and Trends in Machine Learning},
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316 volume = {2},
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317 number = {1},
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318 year = {2009},
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319 pages = {1--127},
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320 note = {Also published as a book. Now Publishers, 2009.},
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321 abstract = {Theoretical results suggest that in order to learn the kind of
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322 complicated functions that can represent high-level abstractions (e.g. in
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323 vision, language, and other AI-level tasks), one may need {\insist deep
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324 architectures}. Deep architectures are composed of multiple levels of non-linear
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325 operations, such as in neural nets with many hidden layers or in complicated
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326 propositional formulae re-using many sub-formulae. Searching the
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327 parameter space of deep architectures is a difficult task, but
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328 learning algorithms such as those for Deep Belief Networks have recently been proposed
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329 to tackle this problem with notable success, beating the state-of-the-art
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330 in certain areas. This paper discusses the motivations and principles regarding
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331 learning algorithms for deep architectures, in particular those exploiting as
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332 building blocks unsupervised learning of single-layer models such as Restricted {Boltzmann} Machines,
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333 used to construct deeper models such as Deep Belief Networks.}
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334 }
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335
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336 @TECHREPORT{Bengio-96-TR,
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337 author = {Bengio, Yoshua},
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338 title = {Using a Financial Training Criterion Rather than a Prediction Criterion},
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339 number = {\#1019},
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340 year = {1996},
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341 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
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342 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/bengioy_TR1019.pdf},
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343 abstract = {The application of this work is to decision taking with financial time-series, using learning algorithms. The traditional approach is to train a model using a rediction criterion, such as minimizing the squared error between predictions and actual values of a dependent variable, or maximizing the likelihood of a conditional model of the dependent variable. We find here with noisy time-series that better results can be obtained when the model is directly trained in order to optimize the financial criterion of interest. Experiments were performed on portfolio selection with 35 Canadian stocks.},
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344 topics={Finance,Discriminant},cat={T},
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345 }
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346
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347 @BOOK{bengio-book96,
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348 author = {Bengio, Yoshua},
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349 title = {Neural Networks for Speech and Sequence Recognition},
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350 year = {1996},
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351 publisher = {International Thompson Computer Press},
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352 topics={Speech},cat={B},
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353 }
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354
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355 @TECHREPORT{Bengio-convex-05,
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356 author = {Bengio, Yoshua and Le Roux, Nicolas and Vincent, Pascal and Delalleau, Olivier and Marcotte, Patrice},
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357 title = {Convex neural networks},
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358 number = {1263},
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359 year = {2005},
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360 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
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361 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/TR1263.pdf},
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362 abstract = {Convexity has recently received a lot of attention in the machine learning community, and the lack of convexity has been seen as a major disadvantage of many learning algorithms, such as multi-layer artificial neural networks. We how that training multi-layer neural networks in which the number of hidden units is learned can be viewed as a convex optimization problem. This problem involves an infinite number of variables, but can be solved by incrementally inserting a hidden unit at a time, each time finding a linear classifiers that minimizes a weighted sum of errors.},
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363 topics={Boosting},cat={T},
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364 }
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365
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366 @ARTICLE{Bengio-decision-trees10,
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367 author = {Bengio, Yoshua and Delalleau, Olivier and Simard, Clarence},
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368 title = {Decision Trees do not Generalize to New Variations},
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369 journal = {Computational Intelligence},
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370 year = {2010},
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371 note = {To appear}
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372 }
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373
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374 @ARTICLE{bengio-demori89,
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375 author = {Bengio, Yoshua and De Mori, Renato},
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376 title = {Use of multilayer networks for the recognition of phonetic features and phonemes},
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377 journal = {Computational Intelligence},
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378 volume = {5},
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379 year = {1989},
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380 pages = {134--141},
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381 topics={Speech},cat={J},
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382 }
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383
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384 @ARTICLE{Bengio-eigen-NC2004,
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385 author = {Bengio, Yoshua and Delalleau, Olivier and Le Roux, Nicolas and Paiement, Jean-Fran{\c c}ois and Vincent, Pascal and Ouimet, Marie},
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386 title = {Learning eigenfunctions links spectral embedding and kernel {PCA}},
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387 journal = {Neural Computation},
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388 volume = {16},
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389 number = {10},
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390 year = {2004},
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391 pages = {2197--2219},
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392 abstract = {In this paper, we show a direct relation between spectral embedding methods and kernel {PCA}, and how both are special cases of a more general learning problem, that of learning the principal eigenfunctions of an operator defined from a kernel and the unknown data generating density. Whereas spectral embedding methods only provided coordinates for the training points, the analysis justifies a simple extension to out-of-sample examples (the Nystr{\"{o}}m formula) for Multi-Dimensional Scaling, spectral clustering, Laplacian eigenmaps, Locally Linear Embedding ({LLE}) and Isomap. The analysis provides, for all such spectral embedding methods, the definition of a loss function, whose empirical average is minimized by the traditional algorithms. The asymptotic expected value of that loss defines a generalization performance and clarifies what these algorithms are trying to learn. Experiments with {LLE}, Isomap, spectral clustering and {MDS} show that this out-of-sample embedding formula generalizes well, with a level of error comparable to the effect of small perturbations of the training set on the embedding.},
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393 topics={HighDimensional,Kernel,Unsupervised},cat={J},
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394 }
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395
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396 @INPROCEEDINGS{Bengio-Gingras-nips8,
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397 author = {Bengio, Yoshua and Gingras, Fran{\c c}ois},
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398 title = {Recurrent Neural Networks for Missing or Asynchronous Data},
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399 year = {1996},
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400 pages = {395--401},
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401 crossref = {NIPS8-shorter},
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402 abstract = {In this paper we propose recurrent neural networks with feedback into the input units for handling two types of data analysis problems. On the one hand, this scheme can be used for static data when some of the input variables are missing. On the other hand, it can also be used for sequential data, when some of the input variables are missing or are available at different frequencies. Unlike in the case of probabilistic models (e.g. Gaussian) of the missing variables, the network does not attempt to model the distribution of the missing variables given the observed variables. Instead it is a more discriminant approach that fills in the missing variables for the sole purpose of minimizing a learning criterion (e.g., to minimize an output error).},
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403 topics={Finance,Missing},cat={C},
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404 }
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405
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406 @ARTICLE{Bengio-Grandvalet-JMLR-04,
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407 author = {Bengio, Yoshua and Grandvalet, Yves},
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408 title = {No Unbiased Estimator of the Variance of K-Fold Cross-Validation},
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409 volume = {5},
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410 year = {2004},
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parents:
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411 pages = {1089--1105},
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412 journal = {Journal of Machine Learning Research},
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413 abstract = {Most machine learning researchers perform quantitative experiments to estimate generalization error and compare the performance of different algorithms (in particular, their proposed algorithm). In order to be able to draw statistically convincing conclusions, it is important to estimate the uncertainty of such estimates. This paper studies the very commonly used K-fold cross-validation estimator of generalization performance. The main theorem shows that there exists no universal (valid under all distributions) unbiased estimator of the variance of K-fold cross-validation. The analysis that accompanies this result is based on the eigen-decomposition of the covariance matrix of errors, which has only three different eigenvalues corresponding to three degrees of freedom of the matrix and three components of the total variance. This analysis helps to better understand the nature of the problem and how it can make naive estimators (that don’t take into account the error correlations due to the overlap between training and test sets) grossly underestimate variance. This is confirmed by numerical experiments in which the three components of the variance are compared when the difficulty of the learning problem and the number of folds are varied.},
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414 topics={Comparative},cat={J},
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415 }
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416
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417 @TECHREPORT{bengio-hyper-TR99,
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418 author = {Bengio, Yoshua},
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419 title = {Continuous Optimization of Hyper-Parameters},
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420 number = {1144},
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421 year = {1999},
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422 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
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423 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/hyperTR.pdf},
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424 abstract = {Many machine learning algorithms can be formulated as the minimization of a training criterion which involves (1) “training errors” on each training example and (2) some hyper-parameters, which are kept fixed during this minimization. When there is only a single hyper-parameter one can easily explore how its value aects a model selection criterion (that is not the same as the training criterion, and is used to select hyper-parameters). In this paper we present a methodology to select many hyper-parameters that is based on the computation of the gradient of a model selection criterion with respect to the hyper-parameters. We first consider the case of a training criterion that is quadratic in the parameters. In that case, the gradient of the selection criterion with respect to the hyper-parameters is efficiently computed by back-propagating through a Cholesky decomposition. In the more general case, we show that the implicit function theorem can be used to derive a formula for the hyper-parameter gradient, but this formula requires the computation of second derivatives of the training criterion},
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425 topics={ModelSelection},cat={T},
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426 }
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427
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428 @INPROCEEDINGS{Bengio-icnn93,
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429 author = {Bengio, Yoshua and Frasconi, Paolo and Simard, Patrice},
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430 title = {The problem of learning long-term dependencies in recurrent networks},
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431 booktitle = {IEEE International Conference on Neural Networks},
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432 year = {1993},
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433 pages = {1183--1195},
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434 publisher = {IEEE Press},
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435 note = {(invited paper)},
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436 topics={LongTerm},cat={C},
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437 }
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438
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439 @ARTICLE{Bengio-ijprai93,
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440 author = {Bengio, Yoshua},
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441 title = {A Connectionist Approach to Speech Recognition},
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442 journal = {International Journal on Pattern Recognition and Artificial Intelligence},
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443 volume = {7},
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444 number = {4},
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445 year = {1993},
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446 pages = {647--668},
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447 abstract = {The task discussed in this paper is that of learning to map input sequences to output sequences. In particular, problems of phoneme recognition in continuous speech are considered, but most of the discussed techniques could be applied to other tasks, such as the recognition of sequences of handwritten characters. The systems considered in this paper are based on connectionist models, or artificial neural networks, sometimes combined with statistical techniques for recognition of sequences of patterns, stressing the integration of prior knowledge and learning. Different architectures for sequence and speech recognition are reviewed, including recurrent networks as well as hybrid systems involving hidden {Markov} models.},
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448 topics={PriorKnowledge,Speech},cat={J},
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449 }
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450
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451 @TECHREPORT{Bengio-iohmms-TR99,
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452 author = {Bengio, Yoshua and Lauzon, Vincent-Philippe and Ducharme, R{\'{e}}jean},
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453 title = {Experiments on the Application of {IOHMM}s to Model Financial Returns Series},
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454 number = {1146},
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455 year = {1999},
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456 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
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457 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/iohmms-returnsTR.pdf},
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458 abstract = {Input/Output Hidden {Markov} Models ({IOHMM}s) are conditional hidden {Markov} models in which the emission (and possibly the transition) probabilities can be conditionned on an input sequence. For example, these conditional distributions can be linear, logistic, or non-linear (using for example multi-layer neural networks). We compare the generalization performance of several models which are special cases of Input/Output Hidden {Markov} Models on financial time-series prediction tasks: an unconditional Gaussian, a conditional linear Gaussian, a mixture of Gaussians, a mixture of conditional linear Gaussians, a hidden {Markov} model, and various {IOHMM}s. The experiments are performed on modeling the returns of market and sector indices. Note that the unconditional Gaussian estimates the first moment with the historical average. The results show that, although for the first moment the historical average gives the best results, for the higher moments, the {IOHMM}s yielded significantly better performance, as measured by the out-of-sample likelihood.},
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459 topics={Markov},cat={T},
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460 }
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461
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462 @ARTICLE{bengio-lauzon-ducharme:2000,
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463 author = {Bengio, Yoshua and Lauzon, Vincent-Philippe and Ducharme, R{\'{e}}jean},
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464 title = {Experiments on the Application of {IOHMM}s to Model Financial Returns Series},
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465 journal = {IEEE Transaction on Neural Networks},
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466 volume = {12},
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467 number = {1},
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468 year = {2001},
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469 pages = {113--123},
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470 abstract = {Input/Output Hidden {Markov} Models ({IOHMM}s) are conditional hidden {Markov} models in which the emission (and possibly the transition) probabilities can be conditioned on an input sequence. For example, these conditional distributions can be logistic, or non-linear (using for example multi-layer neural networks). We compare generalization performance of several models which are special cases of Input/Output Hidden {Markov} Models on financial time-series prediction tasks: an unconditional Gaussian, a conditional linear Gaussian, a mixture of Gaussians, a mixture of conditional linear Gaussians, a hidden {Markov} model, and various {IOHMM}s. The experiments compare these models on predicting the conditional density of returns of market sector indices. Note that the unconditional Gaussian estimates the first moment the historical average. The results show that_ although for the first moment the historical average gives the best results, for the higher moments, the {IOHMM}s significantly better performance, as estimated by the out-of-sample likelihood.},
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471 topics={Markov,Finance},cat={J},
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472 }
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473
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474 @INPROCEEDINGS{bengio-lecun-94,
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475 author = {Bengio, Yoshua and {LeCun}, Yann},
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476 title = {Word normalization for on-line handwritten word recognition},
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477 booktitle = {Proc. of the International Conference on Pattern Recognition},
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478 volume = {II},
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479 year = {1994},
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480 pages = {409--413},
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481 publisher = {IEEE},
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482 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/icpr-norm.ps},
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483 abstract = {We introduce a new approach to normalizing words written with an electronic stylus that applies to all styles of handwriting (upper case, lower case, printed, cursive, or mixed). A geometrical model of the word spatial structure is fitted to the pen trajectory using the {EM} algorithm. The fitting process maximizes the likelihood of the trajectory given the model and a set a priors on its parameters. The method was evaluated and integrated to a recognition system that combines neural networks and hidden {Markov} models.},
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484 topics={PriorKnowledge,Speech},cat={C},
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485 }
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486
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487 @TECHREPORT{Bengio-localfailure-TR-2005,
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488 author = {Bengio, Yoshua and Delalleau, Olivier and Le Roux, Nicolas},
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489 title = {The Curse of Dimensionality for Local Kernel Machines},
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490 number = {1258},
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491 year = {2005},
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492 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
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493 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/tr1258.pdf},
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494 abstract = {We present a series of theoretical arguments supporting the claim that a large class of modern learning algorithms based on local kernels are sensitive to the curse of dimensionality. These include local manifold learning algorithms such as Isomap and {LLE}, support vector classifiers with Gaussian or other local kernels, and graph-based semisupervised learning algorithms using a local similarity function. These algorithms are shown to be local in the sense that crucial properties of the learned function at x depend mostly on the neighbors of x in the training set. This makes them sensitive to the curse of dimensionality, well studied for classical non-parametric statistical learning. There
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495 is a large class of data distributions for which non-local solutions could be expressed compactly and potentially be learned with few examples, but which will require a large number of local bases and therefore a large number of training examples when using a local learning algorithm.},
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496 topics={HighDimensional,Kernel,Unsupervised},cat={T},
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497 }
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498
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499 @INPROCEEDINGS{Bengio-nips-2006,
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500 author = {Bengio, Yoshua and Lamblin, Pascal and Popovici, Dan and Larochelle, Hugo},
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501 title = {Greedy Layer-Wise Training of Deep Networks},
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502 year = {2007},
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503 pages = {153--160},
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parents:
diff changeset
504 crossref = {NIPS19-shorter},
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505 abstract = {Complexity theory of circuits strongly suggests that deep architectures can be
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506 much more efficient (sometimes exponentially) than shallow architectures,
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507 in terms of computational elements required to represent some functions.
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508 Deep multi-layer neural networks have many levels of non-linearities
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509 allowing them to compactly represent highly non-linear and
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510 highly-varying functions. However, until recently it was not clear how
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511 to train such deep networks, since gradient-based
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512 optimization starting from random initialization appears to often get stuck
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513 in poor solutions. Hinton et al. recently introduced
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514 a greedy layer-wise unsupervised learning algorithm for Deep Belief
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515 Networks (DBN), a generative model with many layers of hidden causal
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516 variables. In the context of the above optimization problem,
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517 we study this algorithm empirically and explore variants to
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518 better understand its success and extend it to cases where the inputs are
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diff changeset
519 continuous or where the structure of the input distribution is not
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520 revealing enough about the variable to be predicted in a supervised task.
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521 Our experiments also confirm the hypothesis that the greedy
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522 layer-wise unsupervised training strategy mostly helps the
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523 optimization, by initializing weights in a region near a
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524 good local minimum, giving rise to internal distributed representations
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525 that are high-level abstractions of the input, bringing better generalization.}
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526 }
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527
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528 @INPROCEEDINGS{Bengio-nips10,
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529 author = {Bengio, Yoshua and Bengio, Samy and Isabelle, Jean-Fran{\c c}ois and Singer, Yoram},
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530 title = {Shared Context Probabilistic Transducers},
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531 year = {1998},
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532 crossref = {NIPS10-shorter},
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533 abstract = {Recently, a model for supervised learning of probabilistic transducers represented by suffix trees was introduced. However, this algorithm tends to build very large trees, requiring very large amounts of computer memory. In this paper, we propose a new, more compact, transducer model in which one shares the parameters of distributions associated to contexts yielding similar conditional output distributions. We illustrate the advantages of the proposed algorithm with comparative experiments on inducing a noun phrase recognizer.},
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534 topics={HighDimensional},cat={C},
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535 }
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536
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537 @TECHREPORT{Bengio-NLMP-TR-2005,
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538 author = {Bengio, Yoshua and Larochelle, Hugo},
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539 title = {Non-Local Manifold Parzen Windows},
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540 number = {1264},
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541 year = {2005},
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542 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
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543 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/NLMP-techreport.pdf},
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544 abstract = {In order to escape from the curse of dimensionality, we claim that one can learn non-local functions, in the sense that the value and shape of the learned function at x must be inferred using examples that may be far from x. With this objective, we present a non-local non-parametric density estimator. It builds upon previously proposed Gaussian mixture models with regularized covariance matrices to take into account the local shape of the manifold. It also builds upon recent work on non-local estimators of the tangent plane of a manifold, which are able to generalize in places with little training data, unlike traditional, local, non-parametric models.},
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545 topics={HighDimensional,Kernel,Unsupervised},cat={T},
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546 }
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547
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548 @INPROCEEDINGS{Bengio-nncm96,
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549 author = {Bengio, Yoshua},
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550 editor = {Weigend, A.S. and Abu-Mostafa, Y.S. and Refenes, A. -P. N.},
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551 title = {Training A Neural Network with a Financial Criterion Rather than a Prediction Criterion},
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552 booktitle = {Proceedings of the Fourth International Conference on Neural Networks in the Capital Markets ({NNCM}-96)},
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553 year = {1997},
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554 pages = {433--443},
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555 publisher = {World Scientific},
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556 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/nncm.pdf},
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557 abstract = {A common approach to quantitative decision taking with financial time-series is to train a model using a prediction criterion (e.g., squared error). We find on a portfolio selection problem that better results can be obtained when the model is directly trained in order to optimize the financial criterion of interest, with a differentiable decision module.},
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558 topics={Finance,PriorKnowledge,Discriminant},cat={C},
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559 }
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560
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561 @TECHREPORT{Bengio-NonStat-Hyper-TR,
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562 author = {Bengio, Yoshua and Dugas, Charles},
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563 title = {Learning Simple Non-Stationarities with Hyper-Parameters},
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564 number = {1145},
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565 year = {1999},
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566 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
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567 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/nonstatTR.pdf},
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diff changeset
568 abstract = {We consider sequential data that is sampled from an unknown process, so that the data are not necessarily i.i.d.. Most approaches to machine learning assume that data points are i.i.d.. Instead we consider a measure of generalization that does not make this assumption, and we consider in this context a recently proposed approach to optimizing hyper-parameters, based on the computation of the gradient of a model selection criterion with respect to hyper-parameters. Here we use hyper-parameters that control a function that gives different weights to different time steps in the historical data sequence. The approach is successfully applied to modeling thev olatility of stock returns one month ahead. Comparative experiments with more traditional methods are presented.},
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569 topics={ModelSelection,Finance},cat={T},
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570 }
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571
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572 @ARTICLE{Bengio-scholarpedia-2007,
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573 author = {Bengio, Yoshua},
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parents:
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574 title = {Neural net language models},
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575 journal = {Scholarpedia},
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576 volume = {3},
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577 number = {1},
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578 year = {2008},
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579 pages = {3881},
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580 abstract = {A language model is a function, or an algorithm for learning such a function, that captures the salient statistical characteristics of the distribution of sequences of words in a natural language, typically allowing one to make probabilistic predictions of the next word given preceding ones.
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581
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582 A neural network language model is a language model based on Neural Networks , exploiting their ability to learn distributed representations to reduce the impact of the curse of dimensionality.
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583
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584 In the context of learning algorithms, the curse of dimensionality refers to the need for huge numbers of training examples when learning highly complex functions. When the number of input variables increases, the number of required examples can grow exponentially. The curse of dimensionality arises when a huge number of different combinations of values of the input variables must be discriminated from each other, and the learning algorithm needs at least one example per relevant combination of values. In the context of language models, the problem comes from the huge number of possible sequences of words, e.g., with a sequence of 10 words taken from a vocabulary of 100,000 there are 10^{50} possible sequences...
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585
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586 A distributed representation of a symbol is a tuple (or vector) of features which characterize the meaning of the symbol, and are not mutually exclusive. If a human were to choose the features of a word, he might pick grammatical features like gender or plurality, as well as semantic features like animate" or invisible. With a neural network language model, one relies on the learning algorithm to discover these features, and the features are continuous-valued (making the optimization problem involved in learning much simpler).
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587
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588 The basic idea is to learn to associate each word in the dictionary with a continuous-valued vector representation. Each word corresponds to a point in a feature space. One can imagine that each dimension of that space corresponds to a semantic or grammatical characteristic of words. The hope is that functionally similar words get to be closer to each other in that space, at least along some directions. A sequence of words can thus be transformed into a sequence of these learned feature vectors. The neural network learns to map that sequence of feature vectors to a prediction of interest, such as the probability distribution over the next word in the sequence. What pushes the learned word features to correspond to a form of semantic and grammatical similarity is that when two words are functionally similar, they can be replaced by one another in the same context, helping the neural network to compactly represent a function that makes good predictions on the training set, the set of word sequences used to train the model.
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589
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590 The advantage of this distributed representation approach is that it allows the model to generalize well to sequences that are not in the set of training word sequences, but that are similar in terms of their features, i.e., their distributed representation. Because neural networks tend to map nearby inputs to nearby outputs, the predictions corresponding to word sequences with similar features are mapped to similar predictions. Because many different combinations of feature values are possible, a very large set of possible meanings can be represented compactly, allowing a model with a comparatively small number of parameters to fit a large training set.}
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591 }
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592
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593 @TECHREPORT{Bengio-TR1312,
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594 author = {Bengio, Yoshua},
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595 title = {Learning deep architectures for AI},
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596 number = {1312},
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597 year = {2007},
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598 institution = {Dept. IRO, Universite de Montreal},
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599 note = {Preliminary version of journal article with the same title appearing in Foundations and Trends in Machine Learning (2009)},
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600 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/TR1312.pdf},
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601 abstract = {Theoretical results strongly suggest that in order to learn the kind of
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602 complicated functions that can represent high-level abstractions (e.g. in
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603 vision, language, and other AI-level tasks), one may need deep
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604 architectures. Deep architectures are composed of multiple levels of non-linear
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605 operations, such as in neural nets with many hidden layers. Searching the
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606 parameter space of deep architectures is a difficult optimization task, but
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607 learning algorithms such as those for Deep Belief Networks have recently been proposed
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608 to tackle this problem with notable success, beating the state-of-the-art
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609 in certain areas. This paper discusses the motivations and principles regarding
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diff changeset
610 learning algorithms for deep architectures and in particular for those based
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611 on unsupervised learning such as Deep Belief Networks, using as building
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612 blocks single-layer models such as Restricted {Boltzmann} Machines.}
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613 }
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614
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diff changeset
615 @ARTICLE{Bengio-trnn94,
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616 author = {Bengio, Yoshua and Simard, Patrice and Frasconi, Paolo},
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617 title = {Learning Long-Term Dependencies with Gradient Descent is Difficult},
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618 journal = {IEEE Transactions on Neural Networks},
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619 volume = {5},
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620 number = {2},
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621 year = {1994},
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622 pages = {157--166},
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623 abstract = {Recurrent neural networks can be used to map input sequences to output sequences, such as for recognition, production or prediction problems. However, practical difficulties have been reported in training recurrent neural networks to perform tasks in which the temporal contingencies present in the input/output sequences span long intervals. We show why gradient based learning algorithms face an increasingly difficult problem as the duration of the dependencies to be captures increases. These results expose a trade-off between efficient learning by gradient descent and latching on information for long periods. Based on an understanding of this problem, alternatives to standard gradient descent are considered.},
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624 optnote={(Special Issue on Recurrent Neural Networks)},topics={LongTerm},cat={J},
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625 }
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626
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parents:
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627 @INPROCEEDINGS{Bengio-wirn93,
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628 author = {Bengio, Yoshua and Frasconi, Paolo and Gori, Marco and Soda, G.},
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629 editor = {Caianello, E.},
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630 title = {Recurrent Neural Networks for Adaptive Temporal Processing},
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631 booktitle = {Proc. of the 6th Italian Workshop on Neural Networks, WIRN-93},
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632 year = {1993},
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633 pages = {1183--1195},
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634 publisher = {World Scientific Publ.},
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635 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/rnn_review93.ps},
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636 topics={LongTerm},cat={C},
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637 }
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638
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639 @ARTICLE{Bengio2000c,
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640 author = {Bengio, Yoshua},
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641 title = {Gradient-Based Optimization of Hyperparameters},
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642 journal = {Neural Computation},
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parents:
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643 volume = {12},
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parents:
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644 number = {8},
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parents:
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645 year = {2000},
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646 pages = {1889--1900},
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647 abstract = {Many machine learning algorithms can be formulated as the minimization of a training criterion which involves a hyper-parameter. This hyper-parameter is usually chosen by trial and error with a model selection criterion. In this paper we present a methodology to optimize several hyper-parameters, based on the computation of the gradient of a model selection criterion with respect to the hyper-parameters. In the case of a quadratic training criterion, the gradient of the selection criterion with respect to the hyper-parameters is efficiently computed by back-propagating through a Cholesky decomposition. In the more general case, we show that the implicit function theorem can be used to derive a formula for the hyper-parameter gradient involving second derivatives of the training criterion.},
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648 topics={ModelSelection},cat={J},
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649 }
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650
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651 @ARTICLE{Bengio89a,
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652 author = {Bengio, Yoshua and Cardin, Regis and De Mori, Renato and Merlo, Ettore},
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653 title = {Programmable execution of multi-layered networks for automatic speech recognition},
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654 journal = {Communications of the Association for Computing Machinery},
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655 volume = {32},
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656 number = {2},
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657 year = {1989},
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658 pages = {195--199},
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659 topics={Speech},cat={J},
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660 }
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661
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662 @INPROCEEDINGS{Bengio89c,
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663 author = {Bengio, Yoshua and Cosi, Piero and Cardin, Regis and De Mori, Renato},
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664 title = {Use of multi-layered networks for coding speech with phonetic features},
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665 year = {1989},
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666 pages = {224--231},
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667 crossref = {NIPS1-shorter},
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668 abstract = {Preliminary results on speaker-independant speech recognition are reported. A method that combines expertise on neural networks with expertise on speech recognition is used to build the recognition systems. For transient sounds, event-driven property extractors with variable resolution in the time and frequency domains are used. For sonorant speech, a model of the human auditory system is preferred to FFT as a front-end module.},
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669 topics={Speech},cat={C},
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670 }
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671
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672 @INPROCEEDINGS{Bengio89d,
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673 author = {De Mori, Renato and Bengio, Yoshua and Cosi, Piero},
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674 title = {On the generalization capability of multilayered networks in the extraction of speech properties},
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675 booktitle = {Proceedings of the International Joint Conference on Artificial Intelligence},
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676 year = {1989},
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677 pages = {1531--1536},
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678 publisher = {IEEE},
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679 topics={Speech},cat={C},
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680 }
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681
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682 @INPROCEEDINGS{Bengio90,
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683 author = {Bengio, Yoshua and Cardin, Regis and De Mori, Renato},
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684 title = {Speaker Independent Speech Recognition with Neural Networks and Speech Knowledge},
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685 year = {1990},
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parents:
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686 pages = {218--225},
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687 crossref = {NIPS2-shorter},
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688 abstract = {We attempt to combine neural networks with knowledge from speech science to build a speaker independent speech recognition system. This knowledge is utilized in designing the preprocessing, input coding, output coding, output supervision and architectural constraints. To handle the temporal aspect of speech we combine delays, copies of activations of hidden and output units at the input level, and Back-Propagation for Sequences (BPS), a learning algorithm for networks with local self-loops. This strategy is demonstrated in several experiments, in particular a nasal discrimination task for which the application of a speech theory hypothesis dramatically improved generalization.},
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689 topics={PriorKnowledge,Speech},cat={C},
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690 }
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691
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692 @INCOLLECTION{Bengio90b,
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693 author = {Bengio, Yoshua},
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694 title = {Radial Basis Functions for speech recognition},
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695 booktitle = {Speech Recognition and Understanding: Recent Advances, Trends and Applications},
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diff changeset
696 year = {1990},
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fsavard
parents:
diff changeset
697 pages = {293--298},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
698 publisher = {NATO Advanced Study Institute Series F: Computer and Systems Sciences},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
699 topics={Kernel,Speech},cat={B},
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fsavard
parents:
diff changeset
700 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
701
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
702 @INCOLLECTION{Bengio90c,
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fsavard
parents:
diff changeset
703 author = {Bengio, Yoshua and De Mori, Renato},
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fsavard
parents:
diff changeset
704 editor = {{Fogelman Soulie}, F. and Herault, J.},
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fsavard
parents:
diff changeset
705 title = {Speech coding with multilayer networks},
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fsavard
parents:
diff changeset
706 booktitle = {Neurocomputing: Algorithms, Architectures and Applications},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
707 year = {1990},
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fsavard
parents:
diff changeset
708 pages = {207--216},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
709 publisher = {NATO Advanced Study Institute Series F: Computer and Systems Sciences},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
710 topics={Speech},cat={B},
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fsavard
parents:
diff changeset
711 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
712
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
713 @INPROCEEDINGS{Bengio90e,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
714 author = {Bengio, Yoshua and Pouliot, Yannick and Bengio, Samy and Agin, Patrick},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
715 title = {A neural network to detect homologies in proteins},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
716 year = {1990},
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fsavard
parents:
diff changeset
717 pages = {423--430},
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fsavard
parents:
diff changeset
718 crossref = {NIPS2-shorter},
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fsavard
parents:
diff changeset
719 abstract = {In order to detect the presence and location of immunoglobulin (Ig) domains from amino acid sequences we built a system based on a neural network with one hidden layer trained with back propagation. The program was designed to efficiently identify proteins exhibiting such domains, characterized by a few localized conserved regions and a low overall homology. When the National Biomedical Research Foundation (NBRF) NEW protein sequence database was scanned to evaluate the program's performance, we obtained very low rates of false negatives coupled with a moderate rate of false positives.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
720 topics={Bioinformatic,PriorKnowledge},cat={C},
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fsavard
parents:
diff changeset
721 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
722
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
723 @INPROCEEDINGS{Bengio90z,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
724 author = {Bengio, Yoshua and De Mori, Renato and Gori, Marco},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
725 editor = {Caianello, E.},
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fsavard
parents:
diff changeset
726 title = {Experiments on automatic speech recognition using BPS},
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fsavard
parents:
diff changeset
727 booktitle = {Parallel Architectures and Neural Networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
728 year = {1990},
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fsavard
parents:
diff changeset
729 pages = {223--232},
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fsavard
parents:
diff changeset
730 publisher = {World Scientific Publ.},
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fsavard
parents:
diff changeset
731 topics={Speech},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
732 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
733
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
734 @INPROCEEDINGS{Bengio91a,
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fsavard
parents:
diff changeset
735 author = {Bengio, Yoshua and De Mori, Renato and Flammia, Giovanni and Kompe, Ralf},
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fsavard
parents:
diff changeset
736 title = {A comparative study of hybrid acoustic phonetic decoders based on artificial neural networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
737 booktitle = {Proceedings of EuroSpeech'91},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
738 year = {1991},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
739 topics={PriorKnowledge,Speech},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
740 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
741
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
742 @INPROCEEDINGS{Bengio91b,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
743 author = {Bengio, Yoshua and De Mori, Renato and Flammia, Giovanni and Kompe, Ralf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
744 title = {Global Optimization of a Neural Network - Hidden {M}arkov Model Hybrid},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
745 booktitle = {Proceedings of EuroSpeech'91},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
746 year = {1991},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
747 topics={Markov},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
748 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
749
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
750 @INPROCEEDINGS{Bengio91z,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
751 author = {Bengio, Yoshua and De Mori, Renato and Flammia, Giovanni and Kompe, Ralf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
752 title = {Phonetically motivated acoustic parameters for continuous speech recognition using artificial neural networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
753 booktitle = {Proceedings of EuroSpeech'91},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
754 year = {1991},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
755 cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
756 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
757
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
758 @ARTICLE{Bengio92b,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
759 author = {Bengio, Yoshua and De Mori, Renato and Flammia, Giovanni and Kompe, Ralf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
760 title = {Phonetically motivated acoustic parameters for continuous speech recognition using artificial neural networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
761 journal = {Speech Communication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
762 volume = {11},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
763 number = {2--3},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
764 year = {1992},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
765 pages = {261--271},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
766 note = {Special issue on neurospeech},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
767 topics={PriorKnowledge,Speech},cat={J},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
768 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
769
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
770 @INPROCEEDINGS{Bengio92c,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
771 author = {Bengio, Yoshua and De Mori, Renato and Flammia, Giovanni and Kompe, Ralf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
772 title = {Neural Network - Gaussian Mixture Hybrid for Speech Recognition or Density Estimation},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
773 year = {1992},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
774 pages = {175--182},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
775 crossref = {NIPS4-shorter},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
776 abstract = {The subject of this paper is the integration of multi-layered Artificial Neural Networks ({ANN}) with probability density functions such as Gaussian mixtures found in continuous density hlidden {Markov} Models ({HMM}). In the first part of this paper we present an {ANN}/HMM hybrid in which all the parameters or the the system are simultaneously optimized with respect to a single criterion. In the second part of this paper, we study the relationship between the density of the inputs of the network and the density of the outputs of the networks. A rew experiments are presented to explore how to perform density estimation with {ANN}s.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
777 topics={Speech},cat={C},
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fsavard
parents:
diff changeset
778 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
779
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
780 @INPROCEEDINGS{Bengio94d,
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fsavard
parents:
diff changeset
781 author = {Frasconi, Paolo and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
782 title = {An {EM} Approach to Grammatical Inference: Input/Output {HMMs}},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
783 booktitle = {International Conference on Pattern Recognition (ICPR'94)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
784 year = {1994},
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fsavard
parents:
diff changeset
785 pages = {289--294},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
786 topics={Markov,LongTerm},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
787 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
788
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
789 @ARTICLE{Bengio96,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
790 author = {Bengio, Yoshua and Frasconi, Paolo},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
791 title = {Input/{O}utput {HMM}s for Sequence Processing},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
792 journal = {IEEE Transactions on Neural Networks},
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fsavard
parents:
diff changeset
793 volume = {7},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
794 number = {5},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
795 year = {1996},
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fsavard
parents:
diff changeset
796 pages = {1231--1249},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
797 abstract = {We consider problems of sequence processing and propose a solution based on a discrete state model in order to represent past context. We introduce a recurrent connectionist architecture having a modular structure that associates a subnetwork to each state. The model has a statistical interpretation we call Input/Output Hidden {Markov} Model ({IOHMM}). It can be trained by the {EM} or {GEM} algorithms, considering state trajectories as missing data, which decouples temporal credit assignment and actual parameter estimation.
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
798 The model presents similarities to hidden {Markov} models ({HMM}s), but allows us to map input sequences to output sequences, using the same processing style as recurrent neural networks. {IOHMM}s are trained using a more discriminant learning paradigm than {HMM}s, while potentially taking advantage of the {EM} algorithm.
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
799 We demonstrate that {IOHMM}s are well suited for solving grammatical inference problems on a benchmark problem. Experimental results are presented for the seven Tomita grammars, showing that these adaptive models can attain excellent generalization.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
800 topics={Markov},cat={J},
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fsavard
parents:
diff changeset
801 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
802
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
803 @TECHREPORT{Bengio96-hmmsTR,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
804 author = {Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
805 title = {Markovian Models for Sequential Data},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
806 number = {1049},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
807 year = {1996},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
808 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
809 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/hmmsTR.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
810 abstract = {Hidden {Markov} Models ({HMM}s) are statistical models of sequential data that have been used successfully in many applications, especially for speech recognition. We first summarize the basics of {HMM}s, and then review several recent related learning algorithms and extensions of {HMM}s, including hybrids of {HMM}s with artificial neural networks, Input-Output {HMM}s, weighted transducers, variable-length {Markov} models and {Markov} switching state-space models. Finally, we discuss some of the challenges of future research in this area.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
811 topics={Markov},cat={T},
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fsavard
parents:
diff changeset
812 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
813
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
814 @ARTICLE{Bengio97,
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fsavard
parents:
diff changeset
815 author = {Bengio, Yoshua},
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fsavard
parents:
diff changeset
816 title = {Using a Financial Training Criterion Rather than a Prediction Criterion},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
817 journal = {International Journal of Neural Systems},
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fsavard
parents:
diff changeset
818 volume = {8},
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fsavard
parents:
diff changeset
819 number = {4},
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fsavard
parents:
diff changeset
820 year = {1997},
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fsavard
parents:
diff changeset
821 pages = {433--443},
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fsavard
parents:
diff changeset
822 note = {Special issue on noisy time-series},
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fsavard
parents:
diff changeset
823 abstract = {The application of this work is to decision taking with financial time-series, using learning algorithms. The traditional approach is to train a model using a prediction criterion, such as minimizing the squared error between predictions and actual values of a dependent variable, or maximizing the likelihood of a conditional model of the dependent variable. We find here with noisy time-series that better results can be obtained when the model is directly trained in order to maximize the financial criterion of interest, here gains and losses (including those due to transactions) incurred during trading. Experiments were performed on portfolio selection with 35 Canadian stocks.},
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
824 topics={Finance,PriorKnowledge,Discriminant},cat={J},
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fsavard
parents:
diff changeset
825 }
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fsavard
parents:
diff changeset
826
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
827 @ARTICLE{Bengio99a,
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fsavard
parents:
diff changeset
828 author = {Bengio, Yoshua},
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fsavard
parents:
diff changeset
829 title = {Markovian Models for Sequential Data},
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fsavard
parents:
diff changeset
830 journal = {Neural Computing Surveys},
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fsavard
parents:
diff changeset
831 volume = {2},
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fsavard
parents:
diff changeset
832 year = {1999},
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fsavard
parents:
diff changeset
833 pages = {129--162},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
834 abstract = {Hidden {Markov} Models ({HMM}s) are statistical models of sequential data that have been used successfully in many machine learning applications, especially for speech recognition. Furthermore? in the last few years, many new and promising probabilistic models related to {HMM}s have been proposed. We first summarize the basics of {HMM}s, arid then review several recent related learning algorithms and extensions of {HMM}s, including in particular hybrids of {HMM}s with artificial neural networks, Input-Output {HMM}s (which are conditional {HMM}s using neural networks to compute probabilities), weighted transducers, variable-length {Markov} models and {Markov} switching state-space models. Finally, we discuss some of the challenges of future research in this very active area.},
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parents:
diff changeset
835 topics={Markov},cat={J},
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parents:
diff changeset
836 }
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fsavard
parents:
diff changeset
837
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fsavard
parents:
diff changeset
838 @ARTICLE{Bengio99b,
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fsavard
parents:
diff changeset
839 author = {Bengio, Samy and Bengio, Yoshua and Robert, Jacques and B{\'{e}}langer, Gilles},
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parents:
diff changeset
840 title = {Stochastic Learning of Strategic Equilibria for Auctions},
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fsavard
parents:
diff changeset
841 journal = {Neural Computation},
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fsavard
parents:
diff changeset
842 volume = {11},
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parents:
diff changeset
843 number = {5},
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fsavard
parents:
diff changeset
844 year = {1999},
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fsavard
parents:
diff changeset
845 pages = {1199--1209},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
846 abstract = {This paper presents a new application of stochastic adaptive learning algorithms to the computation of strategic equilibria in auctions. The proposed approach addresses the problems of tracking a moving target and balancing exploration (of action space) versus exploitation (of better modeled regions of action space). Neural networks are used to represent a stochastic decision model for each bidder. Experiments confirm the correctness and usefulness of the approach.},
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fsavard
parents:
diff changeset
847 topics={Auction},cat={J},
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fsavard
parents:
diff changeset
848 }
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fsavard
parents:
diff changeset
849
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
850 @TECHREPORT{bengio:1990,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
851 author = {Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
852 title = {Learning a Synaptic Learning Rule},
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fsavard
parents:
diff changeset
853 number = {751},
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fsavard
parents:
diff changeset
854 year = {1990},
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fsavard
parents:
diff changeset
855 institution = {D{\'{e}}partement d'Informatique et de Recherche Op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
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fsavard
parents:
diff changeset
856 topics={BioRules},cat={T},
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fsavard
parents:
diff changeset
857 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
858
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
859 @INPROCEEDINGS{bengio:1990:snowbird,
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fsavard
parents:
diff changeset
860 author = {Bengio, Yoshua and R., De Mori},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
861 title = {Recurrent networks with Radial Basis Functions for speech recognition},
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fsavard
parents:
diff changeset
862 booktitle = {1990 Neural Networks for Computing Conference},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
863 year = {1990},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
864 topics={Speech},cat={C},
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fsavard
parents:
diff changeset
865 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
866
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
867 @INPROCEEDINGS{bengio:1991:ijcnn,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
868 author = {Bengio, Yoshua and Bengio, Samy and Cloutier, Jocelyn},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
869 title = {Learning a Synaptic Learning Rule},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
870 booktitle = {Proceedings of the International Joint Conference on Neural Networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
871 year = {1991},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
872 pages = {II--A969},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
873 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/bengio_1991_ijcnn.ps},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
874 abstract = {This paper presents an original approach to neural modeling based on the idea of searching, with learning methods, for a synaptic learning rule which is biologically plausible, and yields networks that are able to learn to perform difficult tasks. The proposed method of automatically finding the learning rule relies on the idea of considering the synaptic modification rule as a parametric function. This function has local inputs and is the same in many neurons. The parameters that define this function can be estimated with known learning methods. For this optimization, we give particular attention to gradient descent and genetic algorithms. In both cases, estimation of this function consists of a joint global optimization of (a) the synaptic modification function, and (b) the networks that are learning to perform some tasks. The proposed methodology can be used as a tool to explore the missing pieces of the puzzle of neural networks learning. Both network architecture, and the learning function can be designed within constraints derived from biological knowledge.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
875 addressfr={Seattle, USA},topics={BioRules},cat={C},
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fsavard
parents:
diff changeset
876 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
877
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
878 @INPROCEEDINGS{bengio:1991:nnc,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
879 author = {Bengio, Yoshua and Bengio, Samy and Cloutier, Jocelyn},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
880 title = {Learning Synaptic Learning Rules},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
881 booktitle = {Neural Networks for Computing},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
882 year = {1991},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
883 addressfr={Snowbird, Utah, USA},topics={BioRules},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
884 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
885
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
886 @INPROCEEDINGS{bengio:1991:snowbird,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
887 author = {Bengio, Yoshua and Bengio, Samy and Cloutier, Jocelyn},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
888 title = {Learning a Synaptic Learning Rule},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
889 booktitle = {1991 Neural Networks for Computing Conference},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
890 year = {1991},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
891 topics={BioRules},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
892 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
893
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
894 @INPROCEEDINGS{bengio:1992:nn,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
895 author = {Bengio, Samy and Bengio, Yoshua and Cloutier, Jocelyn and Gecsei, Jan},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
896 title = {Aspects th{\'{e}}oriques de l'optimisation d'une r{\`{e}}gle d'apprentissage},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
897 booktitle = {Actes de la conf{\'{e}}rence Neuro-N{\^{\i}}mes 1992},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
898 year = {1992},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
899 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/bengio_1992_nn.ps},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
900 abstract = {Ayant expos{\'{e}} dans de pr{\'{e}}c{\'{e}}dentes publications (voir [Beng90, Beng92] notamment) l’id{\'{e}}e que l’on pouvait optimiser des r{\`{e}}gles d’apprentissage param{\'{e}}triques pour r{\'{e}}seaux de neurones, nous montrons dans cet article comment d{\'{e}}velopper, par la m{\'{e}}thode du Lagrangien, le gradient n{\'{e}}cessaire {\`{a}} l’optimisation d’une r{\`{e}}gle d’apprentissage par descente du gradient. Nous pr{\'{e}}sentons aussi les bases th{\'{e}}oriques qui permettent d’{\'{e}}tudier la g{\'{e}}n{\'{e}}ralisation {\`{a}} de nouvelles t{\^{a}}ches d’une r{\`{e}}gle d’apprentissage dont les param{\`{e}}tres ont {\'{e}}t{\'{e}} estim{\'{e}}s {\`{a}} partir d’un certain ensemble de t{\^{a}}ches. Enfin, nous exposons bri{\`{e}}vement les r{\'{e}}sultats d’une exp{\'{e}}rience consistant {\`{a}} trouver, par descente du gradient, une r{\`{e}}gle d’apprentissage pouvant r{\'{e}}soudre plusieurs t{\^{a}}ches bool{\'{e}}ennes lin{\'{e}}airement et non lin{\'{e}}airement s{\'{e}}parables.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
901 addressfr={N{\^i}es, France},topics={BioRules},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
902 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
903
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
904 @INPROCEEDINGS{bengio:1992:oban,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
905 author = {Bengio, Samy and Bengio, Yoshua and Cloutier, Jocelyn and Gecsei, Jan},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
906 title = {On the Optimization of a Synaptic Learning rule},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
907 booktitle = {Conference on Optimality in Biological and Artificial Networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
908 year = {1992},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
909 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/bengio_1992_oban.ps},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
910 abstract = {This paper presents a new approach to neural modeling based on the idea of using an automated method to optimize the parameters of a synaptic learning rule. The synaptic modification rule is considered as a parametric function. This function has local inputs and is the same in many neurons. We can use standard optimization methods to select appropriate parameters for a given type of task. We also present a theoretical analysis permitting to study the generalization property of such parametric learning rules. By generalization, we mean the possibility for the learning rule to learn to solve new tasks. Experiments were performed on three types of problems: a biologically inspired circuit (for conditioning in Aplysia). Boolean functions (linearly separable as well as non linearly separable) and classification tasks. The neural network architecture as well as the form and initial parameter values of the synaptic learning function can be designed using a priori knowledge.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
911 addressfr={Dallas, USA},topics={BioRules},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
912 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
913
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
914 @INPROCEEDINGS{bengio:1992:snowbird,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
915 author = {Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
916 title = {Representations Based on Articulatory Dynamics for Speech Recognition},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
917 booktitle = {1992 Neural Networks for Computing Conference},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
918 year = {1992},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
919 topics={PriorKnowledge,Speech},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
920 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
921
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
922 @INPROCEEDINGS{bengio:1993:icann,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
923 author = {Bengio, Samy and Bengio, Yoshua and Cloutier, Jocelyn and Gecsei, Jan},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
924 editor = {Gielen, S. and Kappen, B.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
925 title = {Generalization of a Parametric Learning Rule},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
926 booktitle = {{ICANN} '93: Proceedings of the International Conference on Artificial Neural Networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
927 year = {1993},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
928 pages = {502},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
929 publisher = {Springer-Verlag},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
930 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/bengio_1993_icann.ps},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
931 abstract = {In previous work ([4,2,1]) we discussed the subject of parametric learning rules for neural networks. In this article, we present a theoretical basis permitting to study the generalization property of a learning rule whose parameters are estimated from a set of learning tasks. By generalization, we mean the possibility of using the learning rule to learn solve new tasks. Finally, we describe simple experiments on two-dimensional categorization tasks and show how they corroborate the theoretical results.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
932 addressfr={Amsterdam, Pays-Bas},topics={BioRules},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
933 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
934
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
935 @INPROCEEDINGS{bengio:1993:snowbird,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
936 author = {Bengio, Yoshua and Simard, Patrice and Frasconi, Paolo},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
937 title = {The Problem of Learning Long-Term Dependencies in Recurrent Networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
938 booktitle = {1993 Neural Networks for Computing Conference},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
939 year = {1993},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
940 topics={LongTerm},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
941 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
942
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
943 @TECHREPORT{bengio:1994,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
944 author = {Bengio, Yoshua and Frasconi, Paolo},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
945 title = {An {EM} Approach to Learning Sequential Behavior},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
946 number = {DSI 11-94},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
947 year = {1994},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
948 institution = {Universita di Firenze, Dipartimento di Sistemi e Informatica},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
949 topics={LongTerm},cat={T},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
950 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
951
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
952 @INPROCEEDINGS{bengio:1994:acfas,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
953 author = {Bengio, Samy and Bengio, Yoshua and Cloutier, Jocelyn and Gecsei, Jan},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
954 title = {Optimisation d'une r{\`{e}}gle d'apprentissage pour r{\'{e}}seaux de neurones artificiels},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
955 booktitle = {Actes du soixante-deuxi{\`{e}}me congr{\`{e}}s de l'Association Canadienne Fran{\c c}aise pour l'Avancement des Sciences, colloque sur l'apprentissage et les r{\'{e}}seaux de neurones artificiels},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
956 year = {1994},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
957 topics={BioRules},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
958 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
959
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
960 @INPROCEEDINGS{bengio:1994:snowbird,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
961 author = {Bengio, Yoshua and Frasconi, Paolo},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
962 title = {An {EM} Algorithm for Target Propagation},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
963 booktitle = {1994 Neural Networks for Computing Conference},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
964 year = {1994},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
965 topics={LongTerm},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
966 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
967
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
968 @INPROCEEDINGS{bengio:1994:wcci,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
969 author = {Bengio, Samy and Bengio, Yoshua and Cloutier, Jocelyn},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
970 title = {Use of Genetic Programming for the Search of a New Learning Rule for Neural Networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
971 booktitle = {Proceedings of the First Conference on Evolutionary Computation, {IEEE} World Congress on Computational Intelligence},
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
972 year = {1994},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
973 pages = {324--327},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
974 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/bengio_1994_wcci.ps},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
975 abstract = {In previous work ([1,2,3]), we explained how to use standard optimization methods such as simulated annealing, gradient descent and genetic algorithms to optimize a parametric function which could be used as a learning rule for neural networks. To use these methods, we had to choose a fixed number of parameters and a rigid form for the learning rule. In this article, we propose to use genetic programming to find not only the values of rule parameters but also the optimal number of parameters and the form of the rule. Experiments on classification tasks suggest genetic programming finds better learning rules than other optimization methods. Furthermore, the best rule found with genetic programming outperformed the well-known backpropagation algorithm for a given set of tasks.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
976 topics={BioRules},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
977 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
978
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
979 @INPROCEEDINGS{bengio:1994b:acfas,
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fsavard
parents:
diff changeset
980 author = {Bengio, Yoshua and Frasconi, Paolo},
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fsavard
parents:
diff changeset
981 title = {R{\'{e}}seaux de neurones {M}arkoviens pour l'inf{\'{e}}rence grammaticale},
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fsavard
parents:
diff changeset
982 booktitle = {Actes du soixante-deuxi{\`{e}}me congr{\`{e}}s de l'Association Canadienne Fran{\c c}aise pour l'Avancement des Sciences, colloque sur l'apprentissage et les r{\'{e}}seaux de neurones artificiels},
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fsavard
parents:
diff changeset
983 year = {1994},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
984 topics={Markov,Language},cat={C},
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fsavard
parents:
diff changeset
985 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
986
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
987 @ARTICLE{bengio:1995:npl,
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fsavard
parents:
diff changeset
988 author = {Bengio, Samy and Bengio, Yoshua and Cloutier, Jocelyn},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
989 title = {On the Search for New Learning Rules for {ANN}s},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
990 journal = {Neural Processing Letters},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
991 volume = {2},
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fsavard
parents:
diff changeset
992 number = {4},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
993 year = {1995},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
994 pages = {26--30},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
995 abstract = {In this paper, we present a framework where a learning rule can be optimized within a parametric learning rule space. We define what we call parametric learning rules and present a theoretical study of their generalization properties when estimated from a set of learning tasks and tested over another set of tasks. We corroborate the results of this study with practical experiments.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
996 topics={BioRules},cat={J},
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fsavard
parents:
diff changeset
997 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
998
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
999 @INCOLLECTION{bengio:1995:oban,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1000 author = {Bengio, Samy and Bengio, Yoshua and Cloutier, Jocelyn and Gecsei, Jan},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1001 editor = {Levine, D. S. and Elsberry, W. R.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1002 title = {{O}n the Optimization of a Synaptic Learning Rule},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1003 booktitle = {Optimality in Biological and Artificial Networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1004 year = {1995},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1005 publisher = {Lawrence Erlbaum},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1006 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/bengio_1995_oban.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1007 abstract = {This paper presents a new approach to neural modeling based on the idea of using an automated method to optimize the parameters of a synaptic learning rule. The synaptic modification rule is considered as a parametric function. This function has local inputs and is the same in many neurons. We can use standard optimization methods to select appropriate parameters for a given type of task. We also present a theoretical analysis permitting to study the generalization property of such parametric learning rules. By generalization, we mean the possibility for the learning rule to learn to solve new tasks. Experiments were performed on three types of problems: a biologically inspired circuit (for conditioning in Aplysia), Boolean functions (linearly separable as well as non linearly separable) and classification tasks. The neural network architecture as well as the form and initial parameter values of the synaptic learning function can be designed using a priori knowledge.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1008 topics={BioRules},cat={B},
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fsavard
parents:
diff changeset
1009 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1010
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1011 @TECHREPORT{bengio:1996:udem,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1012 author = {Bengio, Yoshua and Bengio, Samy},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1013 title = {Training Asynchronous Input/Output Hidden {M}arkov Models},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1014 number = {1013},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1015 year = {1996},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1016 institution = {D{\'{e}}partement d'Informatique et de Recherche Op{\'{e}}rationnelle, Universit{\'{e}}de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1017 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/bengio_1996_udem.ps},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1018 topics={Markov},cat={T},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1019 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1020
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1021 @INPROCEEDINGS{bengio:1997:snowbird,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1022 author = {Bengio, Yoshua and Bengio, Samy and Singer, Yoram and Isabelle, Jean-Fran{\c c}ois},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1023 title = {On the Clusterization of Probabilistic Transducers},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1024 booktitle = {1997 Neural Networks for Computing Conference},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1025 year = {1997},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1026 topics={HighDimensional},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1027 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1028
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1029 @INPROCEEDINGS{bengio:1998:snowbird,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1030 author = {Bengio, Samy and Bengio, Yoshua and Robert, Jacques and B{\'{e}}langer, Gilles},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1031 title = {Stochastic Learning of Strategic Equilibria for Auctions},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1032 booktitle = {Learning Conference},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1033 year = {1998},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1034 topics={Auction},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1035 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1036
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1037 @TECHREPORT{bengio:1998:udem,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1038 author = {Bengio, Samy and Bengio, Yoshua and Robert, Jacques and B{\'{e}}langer, Gilles},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1039 title = {Stochastic Learning of Strategic Equilibria for Auctions},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1040 number = {1119},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1041 year = {1998},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1042 institution = {D{\'{e}}partement d'Informatique et de Recherche Op{\'{e}}rationnelle, Universit{\'{e}}de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1043 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/bengio_1998_udem.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1044 abstract = {This paper presents a new application of stochastic adaptive learning algorithms to the computation of strategic equilibria in auctions. The proposed approach addresses the problems of tracking a moving target and balancing exploration (of action space) versus exploitation (of better modeled regions of action space). Neural networks are used to represent a stochastic decision model for each bidder. Experiments confirm the correctness and usefulness of the approach.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1045 topics={Auction},cat={T},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1046 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1047
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1048 @INPROCEEDINGS{bengio:1999:snowbird,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1049 author = {Bengio, Yoshua and Latendresse, Simon and Dugas, Charles},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1050 title = {Gradient-Based Learning of Hyper-Parameters},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1051 booktitle = {Learning Conference},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1052 year = {1999},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1053 topics={ModelSelection},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1054 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1055
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1056 @INPROCEEDINGS{bengio:1999:titration,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1057 author = {Bengio, Yoshua and Brault, J-J. and Major, Fran{\c c}ois and Neal, R. and Pigeon, Steven},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1058 title = {Learning Algorithms for Sorting Compounds from Titration Curves},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1059 booktitle = {Symposium on New Perspectives for Computer-Aided Drug Design},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1060 year = {1999},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1061 topics={Speech},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1062 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1063
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1064 @ARTICLE{bengio:2000:ieeetrnn,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1065 author = {Bengio, Samy and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1066 title = {Taking on the Curse of Dimensionality in Joint Distributions Using Neural Networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1067 journal = {IEEE Transaction on Neural Networks special issue on data mining and knowledge discovery},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1068 volume = {11},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1069 number = {3},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1070 year = {2000},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1071 pages = {550--557},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1072 abstract = {The curse of dimensionality is severe when modeling high-dimensional discrete data: the number of possible combinations of the variables explodes exponentially. In this paper we propose a new architecture for modeling high-dimensional data that requires resources (parameters and computations) that grow at most as the square of the number of variables, using a multi_layer neural network to represent the joint distribution of the variables as the product of conditional distributions. The neural network can be interpreted as a graphical model without hidden random variables, but in which the conditional distributions are tied through the hidden units. The connectivity of the neural network can be pruned by using dependency tests between the variables (thus reducing significantly the number of parameters). Experiments on modeling the distribution of several discrete data sets show statistically significant improvements over other methods such as naive Bayes and comparable Bayesian networks, and show that significant improvements can be obtained by pruning the network.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1073 topics={HighDimensional,Unsupervised,Mining},cat={J},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1074 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1075
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1076 @INPROCEEDINGS{bengio:2000:nips,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1077 author = {Bengio, Yoshua and Bengio, Samy},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1078 title = {Modeling High-Dimensional Discrete Data with Multi-Layer Neural Networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1079 year = {2000},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1080 pages = {400--406},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1081 crossref = {NIPS12-shorter},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1082 abstract = {The curse of dimensionality is severe when modeling high-dimensional discrete data: the number of possible combinations of the variables explodes exponentially. In this paper we propose a new architecture for modeling high-dimensional data that requires resources (parameters and computations) that grow only at most as the square of the number of variables, using a multi-layer neural network to represent the joint distribution of the variables as the product of conditional distributions. The neural network can be interpreted as a graphical model without hidden random variables, but in which the conditional distributions are tied through the hidden units. The connectivity of the neural network can be pruned by using dependency tests between the variables. Experiments on modeling the distribution of several discrete data sets show statistically significant improvements over other methods such as naive Bayes and comparable Bayesian networks, and show that significant improvements can be obtained by pruning the network.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1083 topics={HighDimensional,Unsupervised},cat={C},
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fsavard
parents:
diff changeset
1084 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1085
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1086 @ARTICLE{bengio:2003,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1087 author = {Bengio, Yoshua and Ducharme, R{\'{e}}jean and Vincent, Pascal and Jauvin, Christian},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1088 title = {A Neural Probabilistic Language Model},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1089 volume = {3},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1090 year = {2003},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1091 pages = {1137--1155},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1092 journal = {Journal of Machine Learning Research},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1093 abstract = {A goal of statistical language modeling is to learn the joint probability function of sequences of words in a language. This is intrinsically difficult because of the curse of dimensionality: a word sequence on which the model will be tested is likely to be different from all the word sequences seen during training. Traditional but very successful approaches based on n-grams obtain generalization by concatenating very short overlapping sequences seen in the training set. We propose to fight the curse of dimensionality by learning a distributed representation for words which allows each training sentence to inform the model about an exponential number of semantically neighboring sentences. The model learns simultaneously (1) a distributed representation for each word along with (2) the probability function for word sequences, expressed in terms of these representations. Generalization is obtained because a sequence of words that has never been seen before gets high probability if it is made of words that are similar (in the sense of having a nearby representation) to words forming an already seen sentence. Training such large models (with millions of parameters) within a reasonable time is itself a significant challenge. We report on experiments using neural networks for the probability function, showing on two text corpora that the proposed approach significantly improves on state-of-the-art n-gram models, and that the proposed approach allows to take advantage of longer contexts.},
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1094 topics={Markov,Unsupervised,Language},cat={J},
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fsavard
parents:
diff changeset
1095 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1096
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1097 @TECHREPORT{bengio:socs-1990,
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fsavard
parents:
diff changeset
1098 author = {Bengio, Yoshua and De Mori, Renato and Flammia, Giovanni and Kompe, Ralf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1099 title = {Global Optimization of a Neural Network - Hidden {M}arkov Model Hybrid},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1100 number = {TR-SOCS-90.22},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1101 year = {1990},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1102 institution = {School of Computer Science, McGill University},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1103 topics={Markov},cat={T},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1104 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1105
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1106 @INPROCEEDINGS{bengioc:1994:acfas,
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fsavard
parents:
diff changeset
1107 author = {Bengio, Yoshua and {LeCun}, Yann},
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1108 title = {Reconnaissance de mots manuscrits avec r{\'{e}}seaux de neurones et mod{\`{e}}les de {M}arkov},
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fsavard
parents:
diff changeset
1109 booktitle = {Actes du soixante-deuxi{\`{e}}me congr{\`{e}}s de l'Association Canadienne Fran{\c c}aise pour l'Avancement des Sciences, colloque sur l'apprentissage et les r{\'{e}}seaux de neurones artificiels},
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fsavard
parents:
diff changeset
1110 year = {1994},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1111 topics={Markov,Speech},cat={C},
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fsavard
parents:
diff changeset
1112 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1113
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1114 @TECHREPORT{Bengio_Bottou92,
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fsavard
parents:
diff changeset
1115 author = {Bengio, Yoshua and Bottou, {L{\'{e}}on}},
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fsavard
parents:
diff changeset
1116 title = {A New Approach to Estimating Probability Density Functions with Artificial Neural Networks},
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parents:
diff changeset
1117 number = {TR-92.02},
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fsavard
parents:
diff changeset
1118 year = {1992},
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fsavard
parents:
diff changeset
1119 institution = {Massachusetts Institute of Technology, Dept. Brain and Cognitive Sciences},
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fsavard
parents:
diff changeset
1120 topics={HighDimensional},cat={T},
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fsavard
parents:
diff changeset
1121 }
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fsavard
parents:
diff changeset
1122
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fsavard
parents:
diff changeset
1123 @INCOLLECTION{bengio_extension_nips_2003,
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fsavard
parents:
diff changeset
1124 author = {Bengio, Yoshua and Paiement, Jean-Fran{\c c}ois and Vincent, Pascal and Delalleau, Olivier and Le Roux, Nicolas and Ouimet, Marie},
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fsavard
parents:
diff changeset
1125 keywords = {dimensionality reduction, eigenfunctions learning, Isomap, kernel {PCA}, locally linear embedding, Nystrom formula, spectral methods},
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fsavard
parents:
diff changeset
1126 title = {Out-of-Sample Extensions for {LLE}, Isomap, {MDS}, Eigenmaps, and Spectral Clustering},
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fsavard
parents:
diff changeset
1127 year = {2004},
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fsavard
parents:
diff changeset
1128 crossref = {NIPS16-shorter},
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fsavard
parents:
diff changeset
1129 abstract = {Several unsupervised learning algorithms based on an eigendecomposition provide either an embedding or a clustering only for given training points, with no straightforward extension for out-of-sample examples short of recomputing eigenvectors. This paper provides a unified framework for extending Local Linear Embedding ({LLE}), Isomap, Laplacian Eigenmaps, Multi-Dimensional Scaling (for dimensionality reduction) as well as for Spectral Clustering. This framework is based on seeing these algorithms as learning eigenfunctions of a data-dependent kernel. Numerical experiments show that the generalizations performed have a level of error comparable to the variability of the embedding algorithms due to the choice of training data.},
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parents:
diff changeset
1130 topics={HighDimensional,Kernel,Unsupervised},cat={C},
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fsavard
parents:
diff changeset
1131 }
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fsavard
parents:
diff changeset
1132
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1133 @ARTICLE{Bengio_Gingras98a,
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parents:
diff changeset
1134 author = {Bengio, Yoshua and Gingras, Fran{\c c}ois and Goulard, Bernard and Lina, Jean-Marc},
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fsavard
parents:
diff changeset
1135 title = {Gaussian Mixture Densities for Classification of Nuclear Power Plant Data},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1136 journal = {Computers and Artificial Intelligence},
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fsavard
parents:
diff changeset
1137 volume = {17},
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parents:
diff changeset
1138 number = {2-3},
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parents:
diff changeset
1139 year = {1998},
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parents:
diff changeset
1140 pages = {189--209},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1141 abstract = {In this paper we are concerned with the application of learning algorithms to the classification of reactor states in nuclear plants. Two aspects must be considered, (1) some types of events (e.g., abnormal or rare) will not appear in the data set, but the system should be able to detect them, (2) not only classification of signals but also their interpretation are important for nuclear plant monitoring. We address both issues with a mixture of mixtures of Gaussians in which some parameters are shared to reflect the similar signals observed in different states of the reactor. An {EM} algorithm for these shared Gaussian mixtures is presented. Experimental results on nuclear plant data demonstrate the advantages of the proposed approach with respect to the above two points.},
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parents:
diff changeset
1142 topics={Mining},cat={J},
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fsavard
parents:
diff changeset
1143 }
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fsavard
parents:
diff changeset
1144
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1145 @ARTICLE{Bengio_Gingras98b,
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parents:
diff changeset
1146 author = {Gingras, Fran{\c c}ois and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1147 title = {Handling Asynchronous or Missing Financial Data with Recurrent Networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1148 journal = {International Journal of Computational Intelligence and Organizations},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1149 volume = {1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1150 number = {3},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1151 year = {1998},
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fsavard
parents:
diff changeset
1152 pages = {154--163},
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fsavard
parents:
diff changeset
1153 abstract = {An important issue with many sequential data analysis problems, such as those encountered in financial data sets, is that different variables are known at different frequencies, at different times (asynchronicity), or are sometimes missing. To address this issue we propose to use recurrent networks with feedback into the input units, based on two fundamental ideas. The first motivation is that the “filled-in” value of the missing variable may not only depend in complicated ways on the value of this variable in the past of the sequence but also on the current and past values of other variables. The second motivation is that, for the purpose of making predictions or taking decisions, it is not always necessary to fill in the best possible value of the missing variables. In fact, it is sufficient to fill in a value which helps the system make better predictions or decisions. The advantages of this approach are demonstrated through experiments on several tasks.},
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fsavard
parents:
diff changeset
1154 topics={Finance,Missing},cat={J},
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fsavard
parents:
diff changeset
1155 }
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fsavard
parents:
diff changeset
1156
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1157 @INPROCEEDINGS{Bengio_icassp90,
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fsavard
parents:
diff changeset
1158 author = {Bengio, Yoshua and Cardin, Regis and De Mori, Renato and Normandin, Yves},
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fsavard
parents:
diff changeset
1159 title = {A Hybrid Coder for Hidden {M}arkov Models Using a Recurrent Neural Network},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1160 booktitle = {International Conference on Acoustics, Speech and Signal Processing},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1161 year = {1990},
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fsavard
parents:
diff changeset
1162 pages = {537--540},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1163 topics={Markov,Speech},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1164 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1165
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1166 @INPROCEEDINGS{Bengio_LeCun94,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1167 author = {Bengio, Yoshua and {LeCun}, Yann and Henderson, Donnie},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1168 title = {Globally Trained Handwritten Word Recognizer using Spatial Representation, Space Displacement Neural Networks and Hidden {M}arkov Models},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1169 year = {1994},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1170 pages = {937--944},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1171 crossref = {NIPS6-shorter},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1172 abstract = {We introduce a new approach for on-line recognition of handwritten words written in unconstrained mixed style. The preprocessor performs a word-level normalization by fitting a model of the word structure using the {EM} algorithm. Words are then coded into low resolution “annotated images” where each pixel contains information about trajectory direction and curvature. The recognizer is a convolution network which can be spatially replicated. From the network output, a hidden {Markov} model produces word scores. The entire system is globally trained to minimize word-level errors.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1173 topics={Speech},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1174 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1175
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1176 @ARTICLE{Bengio_LeCun95,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1177 author = {Bengio, Yoshua and {LeCun}, Yann and Nohl, Craig and Burges, Chris},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1178 title = {LeRec: A {NN}/{HMM} Hybrid for On-Line Handwriting Recognition},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1179 journal = {Neural Computation},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1180 volume = {7},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1181 number = {6},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1182 year = {1995},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1183 pages = {1289--1303},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1184 abstract = {We introduce a new approach for on-line recognition of handwritten words written in unconstrained mixed style. The preprocessor performs a word-level normalization by fitting a model of the word structure using the {EM} algorithm. Words are then coded into low resolution “annotated images” where each pixel contains information about trajectory direction and curvature. The recognizer is a convolution network which can be spatially replicated. From the network output, a hidden {Markov} model produces word scores. The entire system is globally trained to minimize word-level errors.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1185 topics={PriorKnowledge,Speech},cat={J},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1186 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1187
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1188 @ARTICLE{Bengio_prel92,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1189 author = {Bengio, Yoshua and Gori, Marco and De Mori, Renato},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1190 title = {Learning the Dynamic Nature of Speech with Back-propagation for Sequences},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1191 journal = {Pattern Recognition Letters},
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fsavard
parents:
diff changeset
1192 volume = {13},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1193 number = {5},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1194 year = {1992},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1195 pages = {375--385},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1196 note = {(Special issue on Artificial Neural Networks)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1197 topics={Speech},cat={J},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1198 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1199
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1200 @ARTICLE{Bengio_trnn92,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1201 author = {Bengio, Yoshua and De Mori, Renato and Flammia, Giovanni and Kompe, Ralf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1202 title = {Global Optimization of a Neural Network-Hidden {M}arkov Model Hybrid},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1203 journal = {IEEE Transactions on Neural Networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1204 volume = {3},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1205 number = {2},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1206 year = {1992},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1207 pages = {252--259},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1208 topics={Markov},cat={J},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1209 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1210
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1211 @TECHREPORT{Bergstra+2009,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1212 author = {Bergstra, James and Desjardins, Guillaume and Lamblin, Pascal and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1213 title = {Quadratic Polynomials Learn Better Image Features},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1214 number = {1337},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1215 year = {2009},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1216 institution = {D{\'{e}}partement d'Informatique et de Recherche Op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1217 abstract = {The affine-sigmoidal hidden unit (of the form $\sigma(ax+b)$)
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1218 is a crude predictor of neuron response in visual area V1.
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1219 More descriptive models of V1 have been advanced that are no more computationally expensive,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1220 yet artificial neural network research continues to focus on networks of affine-sigmoidal models.
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1221 This paper identifies two qualitative differences between the affine-sigmoidal hidden unit
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1222 and a particular recent model of V1 response:
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1223 a) the presence of a low-rank quadratic term in the argument to $\sigma$,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1224 and b) the use of a gentler non-linearity than the $\tanh$ or logistic sigmoid.
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fsavard
parents:
diff changeset
1225 We evaluate these model ingredients by training single-layer
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1226 neural networks to solve three image classification tasks.
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1227 We experimented with fully-connected hidden units,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1228 as well as locally-connected units and convolutional units
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1229 that more closely mimic the function and connectivity of the visual system.
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1230 On all three tasks, both the quadratic interactions and the gentler non-linearity
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1231 lead to significantly better generalization.
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1232 The advantage of quadratic units was strongest in conjunction with sparse and convolutional hidden units.}
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fsavard
parents:
diff changeset
1233 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1234
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1235 @MISC{bergstra+al:2010-scipy,
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fsavard
parents:
diff changeset
1236 author = {Bergstra, James},
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fsavard
parents:
diff changeset
1237 title = {Optimized Symbolic Expressions and {GPU} Metaprogramming with Theano},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1238 year = {2010},
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fsavard
parents:
diff changeset
1239 howpublished = {{SciPy}},
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fsavard
parents:
diff changeset
1240 note = {Oral}
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fsavard
parents:
diff changeset
1241 }
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fsavard
parents:
diff changeset
1242
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1243 @MISC{bergstra+al:2010-sharcnet,
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fsavard
parents:
diff changeset
1244 author = {Bergstra, James and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1245 title = {{GPU} Programming with Theano},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1246 year = {2010},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1247 howpublished = {{SHARCNET} Research Day},
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fsavard
parents:
diff changeset
1248 note = {Oral}
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fsavard
parents:
diff changeset
1249 }
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fsavard
parents:
diff changeset
1250
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1251 @MISC{bergstra+al:2010snowbird,
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parents:
diff changeset
1252 author = {Bergstra, James and Breuleux, Olivier and Bastien, Fr{\'{e}}d{\'{e}}ric and Lamblin, Pascal and Turian, Joseph and Desjardins, Guillaume and Pascanu, Razvan and Erhan, Dumitru and Delalleau, Olivier and Bengio, Yoshua},
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parents:
diff changeset
1253 title = {Deep Learning on {GPU}s with Theano},
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parents:
diff changeset
1254 booktitle = {The Learning Workshop},
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fsavard
parents:
diff changeset
1255 year = {2010},
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fsavard
parents:
diff changeset
1256 note = {Oral}
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fsavard
parents:
diff changeset
1257 }
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parents:
diff changeset
1258
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1259 @INPROCEEDINGS{Bergstra+Bengio-2009,
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fsavard
parents:
diff changeset
1260 author = {Bergstra, James and Bengio, Yoshua},
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fsavard
parents:
diff changeset
1261 title = {Slow, Decorrelated Features for Pretraining Complex Cell-like Networks},
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fsavard
parents:
diff changeset
1262 year = {2009},
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fsavard
parents:
diff changeset
1263 crossref = {NIPS22}
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fsavard
parents:
diff changeset
1264 }
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fsavard
parents:
diff changeset
1265
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1266 @ARTICLE{bergstra+casagrande+erhan+eck+kegl:2006,
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fsavard
parents:
diff changeset
1267 author = {Bergstra, James and Casagrande, Norman and Erhan, Dumitru and Eck, Douglas and K{\'{e}}gl, Bal{\'{a}}zs},
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parents:
diff changeset
1268 title = {Aggregate Features and AdaBoost for Music Classification},
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parents:
diff changeset
1269 journal = {Machine Learning},
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parents:
diff changeset
1270 volume = {65},
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parents:
diff changeset
1271 year = {2006},
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parents:
diff changeset
1272 pages = {473--484},
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parents:
diff changeset
1273 issn = {0885-6125},
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fsavard
parents:
diff changeset
1274 abstract = {We present an algorithm that predicts musical genre and artist from an audio waveform. Our method uses the ensemble learner ADABOOST to select from a set of audio features that have been extracted from segmented audio and then aggregated. Our classifier proved to be the most effective method for genre classification at the recent MIREX 2005 international contests in music information extraction, and the second-best method for recognizing artists. This paper describes our method in detail, from feature extraction to song classification, and presents an evaluation of our method on three genre databases and two artist-recognition databases. Furthermore, we present evidence collected from a variety of popular features and classifiers that the technique of classifying features aggregated over segments of audio is better than classifying either entire songs or individual short-timescale features.},
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parents:
diff changeset
1275 PDF = {papers/2006_ml_draft.pdf},
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parents:
diff changeset
1276 SOURCE = {OwnPublication},
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parents:
diff changeset
1277 }
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fsavard
parents:
diff changeset
1278
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1279 @INPROCEEDINGS{bergstra+lacoste+eck:2006,
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fsavard
parents:
diff changeset
1280 author = {Bergstra, James and Lacoste, Alexandre and Eck, Douglas},
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fsavard
parents:
diff changeset
1281 title = {Predicting Genre Labels for Artists using FreeDB},
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parents:
diff changeset
1282 booktitle = {Proc. 7th International Conference on Music Information Retrieval (ISMIR)},
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fsavard
parents:
diff changeset
1283 year = {2006},
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fsavard
parents:
diff changeset
1284 SOURCE = {OwnPublication},
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fsavard
parents:
diff changeset
1285 PDF = {papers/2006_ismir_freedb.pdf},
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fsavard
parents:
diff changeset
1286 }
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fsavard
parents:
diff changeset
1287
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1288 @INPROCEEDINGS{bergstra+mandel+eck:2010,
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fsavard
parents:
diff changeset
1289 author = {Bergstra, James and Mandel, Michael and Eck, Douglas},
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fsavard
parents:
diff changeset
1290 title = {Scalable Genre and Tag Prediction with Spectral Covariance},
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fsavard
parents:
diff changeset
1291 booktitle = {{ISMIR}},
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fsavard
parents:
diff changeset
1292 year = {2010},
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fsavard
parents:
diff changeset
1293 note = {accepted}
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fsavard
parents:
diff changeset
1294 }
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fsavard
parents:
diff changeset
1295
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1296 @MASTERSTHESIS{Bergstra-Msc-2006,
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parents:
diff changeset
1297 author = {Bergstra, James},
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fsavard
parents:
diff changeset
1298 keywords = {apprentissage statistique, classification de musique par genre, extraction de caract{\'{e}}ristiques sonores, recherche d'information musicale},
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fsavard
parents:
diff changeset
1299 title = {Algorithms for Classifying Recorded Music by Genre},
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fsavard
parents:
diff changeset
1300 year = {2006},
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fsavard
parents:
diff changeset
1301 school = {Universit{\'{e}} de Montreal},
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fsavard
parents:
diff changeset
1302 abstract = {Ce m{\'{e}}moire traite le probl{\`{e}}me de la classification automatique de signaux musicaux par genre. Dans un premier temps, je pr{\'{e}}sente une technique utilisant l'apprentissage machine pour classifier des statistiques extraites sur des segments du signal sonore. Malgr{\'{e}} le fait que cette technique a d{\'{e}}j{\`{a}} {\'{e}}t{\'{e}} explor{\'{e}}e, mon m{\'{e}}moire est le premier {\`{a}} investiguer l'influence de la longueur et de la quantit{\'{e}} de ces segments sur le taux de classification. J'explore {\'{e}}galement l'importance d'avoir des segments contigus dans le temps. Les segments d'une {\`{a}} trois secondes apportent une meilleure performance, mais pour ce faire, ils doivent {\^{e}}tre suffisamment nombreux. Il peut m{\^{e}}me {\^{e}}tre utile d'augmenter la quantit{\'{e}} de segments jusqu'{\`{a}} ce qu'ils se chevauchent. Dans les m{\^{e}}mes exp{\'{e}}riences, je pr{\'{e}}sente une formulation alternative des descripteurs d'audio nomm{\'{e}}e Melfrequency Cepstral Coefficient (MFCC) qui am{\`{e}}ne un taux de classification de 81 \% sur un jeux de donn{\'{e}}es pour lequel la meilleure performance publi{\'{e}}e est de 71 \%. Cette m{\'{e}}thode de segmentation des chansons, ainsi que cette formulation alternative, ont pour but d'am{\'{e}}liorer l'algorithme gagnant du concours de classification de genre de MIREX 2005, d{\'{e}}velopp{\'{e}} par Norman Casagrande et moi. Ces exp{\'{e}}riences sont un approfondissement du travail entam{\'{e}} par Bergstra et al. [2006a], qui d{\'{e}}crit l'algorithme gagnant de ce concours.
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parents:
diff changeset
1303 Dans un deuxi{\`{e}}me temps, je pr{\'{e}}sent une m{\'{e}}thode qui utilise FreeDB, une base de donn{\'{e}}es d'information sur les albums, pour attribuer {\`{a}} un artiste une distribution de probabilit{\'{e}} sur son genre. Avec une petite base de donn{\'{e}}es, faite {\`{a}} la main, je montre qu'il y a une haute corr{\'{e}}lation entre cette distribution et l'{\'{e}}tiquette de genre traditionnel. Bien qu'il reste {\`{a}} d{\'{e}}montrer que cette m{\'{e}}thode est utile pour organiser une collection de musique, ce r{\'{e}}sultat sugg{\`{e}}re qu'on peut maintenant {\'{e}}tiqueter de grandes bases de musique automatiquement {\`{a}} un faible co{\^{u}}t, et par cons{\'{e}}quent de poursuivre plus facilement la recherche en classification {\`{a}} grande {\'{e}}chelle. Ce travail sera publi{\'{e}} comme Bergstra et al. [2006b] {\`{a}} ISMIR 2006.}
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parents:
diff changeset
1304 }
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parents:
diff changeset
1305
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parents:
diff changeset
1306 @INPROCEEDINGS{bergstra:2010cosyne,
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parents:
diff changeset
1307 author = {Bergstra, James and Bengio, Yoshua and Lamblin, Pascal and Desjardins, Guillaume and Louradour, Jerome},
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parents:
diff changeset
1308 title = {Image classification with complex cell neural networks},
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parents:
diff changeset
1309 booktitle = {Computational and systems neuroscience (COSYNE)},
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fsavard
parents:
diff changeset
1310 year = {2010},
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parents:
diff changeset
1311 note = {Poster},
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parents:
diff changeset
1312 url = {http://www.frontiersin.org/conferences/individual_abstract_listing.php?conferid=770&pap=3626&ind_abs=1&pg=335},
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parents:
diff changeset
1313 doi = {10.3389/conf.fnins.2010.03.00334}
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parents:
diff changeset
1314 }
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parents:
diff changeset
1315
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parents:
diff changeset
1316 @INPROCEEDINGS{biaslearn:2000:ijcnn,
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parents:
diff changeset
1317 author = {Ghosn, Joumana and Bengio, Yoshua},
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parents:
diff changeset
1318 title = {Bias Learning, Knowledge Sharing},
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parents:
diff changeset
1319 booktitle = {International Joint Conference on Neural Networks 2000},
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parents:
diff changeset
1320 volume = {I},
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parents:
diff changeset
1321 year = {2000},
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parents:
diff changeset
1322 pages = {9--14},
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parents:
diff changeset
1323 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/ijcnn_manifold.pdf},
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parents:
diff changeset
1324 abstract = {Biasing the hypothesis space of a learner has been shown to improve generalisation performances. Methods for achieving this goal have been proposed, that range from deriving and introducing a bias into a learner to automatically learning the bias. In the latter case, most methods learn the bias by simultaneously training several related tasks derived from the same domain and imposing constraints on their parameters. We extend some of the ideas presented in this field and describe a new model that parameterizes the parameters of each task as a function of an affine manifold defined in parameter space and a point lying on the manifold. An analysis of variance on a class of learning tasks is performed that shows some significantly improved performances when using the model.},
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parents:
diff changeset
1325 topics={MultiTask},cat={C},
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parents:
diff changeset
1326 }
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parents:
diff changeset
1327
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parents:
diff changeset
1328 @ARTICLE{biaslearn:2003:tnn,
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parents:
diff changeset
1329 author = {Ghosn, Joumana and Bengio, Yoshua},
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parents:
diff changeset
1330 title = {Bias Learning, Knowledge Sharing},
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parents:
diff changeset
1331 journal = {IEEE Transaction on Neural Networks},
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parents:
diff changeset
1332 volume = {14},
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parents:
diff changeset
1333 number = {4},
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parents:
diff changeset
1334 year = {2003},
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parents:
diff changeset
1335 pages = {748--765},
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parents:
diff changeset
1336 abstract = {Biasing properly the hypothesis space of a learner has been shown to improve generalization performance. Methods for achieving this goal have been proposed, that range from designing and introducing a bias into a learner to automatically learning the bias. Multitask learning methods fall into the latter category. When several related tasks derived from the same domain are available, these methods use the domain-related knowledge coded in the training examples of all the tasks as a source of bias. We extend some of the ideas presented in this field and describe a new approach that identifies a family of hypotheses, represented by a manifold in hypothesis space, that embodies domain-related knowledge. This family is learned using training examples sampled from a group of related tasks. Learning models trained on these tasks are only allowed to select hypotheses that belong to the family. We show that the new approach encompasses a large variety of families which can be learned. A statistical analysis on a class of related tasks is performed that shows significantly improved performances when using this approach.},
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parents:
diff changeset
1337 topics={MultiTask},cat={J},
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parents:
diff changeset
1338 }
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fsavard
parents:
diff changeset
1339
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1340 @MASTERSTHESIS{Boisvert-Mcs-2005,
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parents:
diff changeset
1341 author = {Boisvert, Maryse},
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parents:
diff changeset
1342 keywords = {Algorithme {EM} , D{\'{e}}composition en valeurs singuli{\`{e}}res , D{\'{e}}sambigu{\"{\i}}sation s{\'{e}}mantique , Mod{\`{e}}les graphiques, WordNet },
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parents:
diff changeset
1343 title = {R{\'{e}}duction de dimension pour mod{\`{e}}les graphiques probabilistes appliqu{\'{e}}s {\`{a}} la d{\'{e}}sambiguisation s{\'{e}}mantique},
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parents:
diff changeset
1344 year = {2005},
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parents:
diff changeset
1345 school = {Universit{\'{e}} de Montr{\'{e}}al}
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parents:
diff changeset
1346 }
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parents:
diff changeset
1347
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1348 @INPROCEEDINGS{bonneville98,
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parents:
diff changeset
1349 author = {Bonneville, Martin and Meunier, Jean and Bengio, Yoshua and Soucy, Jean-Paul},
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parents:
diff changeset
1350 title = {Support Vector Machines for Improving the classification of Brain Pet Images},
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parents:
diff changeset
1351 booktitle = {SPIE Medical Imaging},
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parents:
diff changeset
1352 year = {1998},
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parents:
diff changeset
1353 topics={Kernel},cat={C},
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parents:
diff changeset
1354 }
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parents:
diff changeset
1355
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1356 @INPROCEEDINGS{Bottou+Bengio95,
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parents:
diff changeset
1357 author = {Bottou, {L{\'{e}}on} and Bengio, Yoshua},
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parents:
diff changeset
1358 title = {Convergence Properties of the {K}-Means Algorithm},
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parents:
diff changeset
1359 year = {1995},
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parents:
diff changeset
1360 pages = {585--592},
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parents:
diff changeset
1361 crossref = {NIPS7-shorter},
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parents:
diff changeset
1362 abstract = {This paper studies the convergence properties of the well known K-Means clustering algorithm. The K-Means algorithm can be described either as a gradient descent algorithm or by slightly extending the mathematics of the {EM} algorithm to this hard threshold case. We show that the K-Means algorithm actually minimizes the quantization error using the very fast Newton algorithm.},
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parents:
diff changeset
1363 topics={Unsupervised},cat={C},
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parents:
diff changeset
1364 }
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parents:
diff changeset
1365
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parents:
diff changeset
1366 @ARTICLE{bottou-98,
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parents:
diff changeset
1367 author = {Bottou, {L{\'{e}}on} and Haffner, Patrick and G. Howard, Paul and Simard, Patrice and Bengio, Yoshua and {LeCun}, Yann},
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parents:
diff changeset
1368 title = {High Quality Document Image Compression with {DjVu}},
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parents:
diff changeset
1369 journal = {Journal of Electronic Imaging},
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parents:
diff changeset
1370 volume = {7},
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parents:
diff changeset
1371 number = {3},
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parents:
diff changeset
1372 year = {1998},
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parents:
diff changeset
1373 pages = {410--425},
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parents:
diff changeset
1374 topics={Compression},cat={J},
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parents:
diff changeset
1375 }
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parents:
diff changeset
1376
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parents:
diff changeset
1377 @INPROCEEDINGS{Bottou-dcc98,
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parents:
diff changeset
1378 author = {Bottou, {L{\'{e}}on} and G. Howard, Paul and Bengio, Yoshua},
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parents:
diff changeset
1379 editor = {Society, {IEEE} Computer},
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parents:
diff changeset
1380 title = {The Z-Coder Adaptive Binary Coder},
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parents:
diff changeset
1381 booktitle = {Data Compression Conference},
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parents:
diff changeset
1382 year = {1998},
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parents:
diff changeset
1383 url = {http://leon.bottou.org/papers/bottou-howard-bengio-98},
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parents:
diff changeset
1384 topics={Compression},cat={C},
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parents:
diff changeset
1385 }
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parents:
diff changeset
1386
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parents:
diff changeset
1387 @INPROCEEDINGS{bottou-lecun-bengio-97,
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fsavard
parents:
diff changeset
1388 author = {Bottou, {L{\'{e}}on} and {LeCun}, Yann and Bengio, Yoshua},
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fsavard
parents:
diff changeset
1389 title = {Global Training of Document Processing Systems using Graph Transformer Networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1390 booktitle = {Proc. of Computer Vision and Pattern Recognition},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1391 year = {1997},
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fsavard
parents:
diff changeset
1392 pages = {490--494},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1393 publisher = {IEEE},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1394 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/bottou-lecun-bengio-97.ps.gz},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1395 topics={PriorKnowledge,Speech},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1396 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1397
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1398 @TECHREPORT{bottou96TR,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1399 author = {Bottou, {L{\'{e}}on} and Bengio, Yoshua and {LeCun}, Yann},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1400 title = {Document analysis with transducers},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1401 number = {Technical Memorandum HA615600-960701-01TM},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1402 year = {1996},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1403 institution = {AT\&T Labs},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1404 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/transducer-tm.ps.gz},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1405 topics={HighDimensional},cat={T},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1406 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1407
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1408 @TECHREPORT{bottou97TR,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1409 author = {Bottou, {L{\'{e}}on} and Bengio, Yoshua and G. Howard, Paul},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1410 title = {Z-Coder: A Fast Adaptive Binary Arithmetic Coder},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1411 number = {Technical Memorandum HA615600-970721-02TM},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1412 year = {1997},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1413 institution = {AT\&T Labs},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1414 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/zcoder-tm.ps.gz},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1415 topics={Compression},cat={T},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1416 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1417
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1418 @MASTERSTHESIS{Bouchard-Msc-2007,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1419 author = {Bouchard, Lysiane},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1420 keywords = {auditory cortex, fMRI, linear classifier, logistic regression, na{\"{\i}}ve bayesian gaussian model, neuroimaging, spectro-temporal modulation, support vectors machine},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1421 title = {Analyse par apprentissage automatique des r{\'{e}}ponses fMRI du cortex auditif {\`{a}} des modulations spectro-temporelles.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1422 year = {2009},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1423 school = {Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1424 abstract = {The application of linear machine learning classifiers to the analysis of brain imaging data (fMRI) has led to several interesting breakthroughs in recent years. These classifiers combine the responses of the voxels to detect and categorize different brain states. They allow a more agnostic analysis than conventional fMRI analysis that systematically treats weak and distributed patterns as unwanted noise. In this project, we use such classifiers to validate an hypothesis concerning the encoding of sounds in the human brain. More precisely, we attempt to locate neurons tuned to spectral and temporal modulations in sound. We use fMRI recordings of brain responses of subjects listening to 49 different spectro-temporal modulations. The analysis of fMRI data through linear classifiers is not yet a standard procedure in this field. Thus, an important objective of this project, in the long term, is the development of new machine learning algorithms specialized for neuroimaging data. For these reasons, an important part of the experiments is dedicated to studying the behaviour of the classifiers. We are mainly interested in 3 standard linear classifiers, namely the support vectors machine algorithm (linear), the logistic regression algorithm (regularized) and the na{\"{\i}}ve bayesian gaussian model (shared variances).}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1425 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1426
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1427 @PHDTHESIS{Boufaden-Phd-2005,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1428 author = {Boufaden, Narj{\`{e}}s},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1429 title = {Extraction d’information {\`{a}} partir de transcriptions de conversations t{\'{e}}l{\'{e}}phoniques sp{\'{e}}cialis{\'{e}}es},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1430 year = {2005},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1431 school = {Universit{\'{e}} de Montr{\'{e}}al, D{\'{e}}partement d'Informatique et de Recherche Op{\'{e}}rationnel}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1432 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1433
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1434 @INPROCEEDINGS{Carreau+Bengio-2007,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1435 author = {Carreau, Julie and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1436 title = {A Hybrid {Pareto} Model for Conditional Density Estimation of Asymmetric Fat-Tail Data},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1437 booktitle = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS'07)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1438 year = {2007},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1439 publisher = {Omnipress},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1440 abstract = {We propose an estimator for the conditional density p(Y|X) that can adapt for asymmetric heavy tails which might depend on X. Such estimators have important applications in finance and insurance. We draw from Extreme Value Theory the tools to build a hybrid unimodal density having a parameter controlling the heaviness of the upper tail. This hybrid is a Gaussian whose upper tail has been replaced by a generalized {Pareto} tail. We use this hybrid in a multi-modal mixture in order to obtain a nonparametric density estimator that can easily adapt for heavy tailed data. To obtain a conditional density estimator, the parameters of the mixture estimator can be seen as
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1441 functions of X and these functions learned. We show experimentally that this approach better models the conditional density in terms of likelihood than compared competing algorithms : conditional mixture models with other types of components and multivariate nonparametric models.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1442 date={21-24}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1443 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1444
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1445 @ARTICLE{Carreau+Bengio-2009,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1446 author = {Carreau, Julie and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1447 title = {A Hybrid {Pareto} Mixture for Conditional Asymmetric Fat-Tailed Distributio\ n},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1448 journal = {IEEE Transactions on Neural Networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1449 volume = {20},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1450 number = {7},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1451 year = {2009},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1452 pages = {1087--1101},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1453 issn = {1045-9227},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1454 abstract = {In many cases, we observe some variables X that contain predictive information over a scalar variable of interest Y, with (X,Y) pairs observed in a training set. We can take advantage of this information to estimate the conditional density P(Y\X = x). In this paper, we propose a conditional mixture model with hybrid {Pareto} components to estimate P(Y\X = x).The hybrid {Pareto} is a Gaussian whose upper tail has been replaced by a generalized {Pareto} tail. A third parameter, in addition to the location and spread parameters of the Gaussian, controls the heaviness of the upper tail. Using the hybrid {Pareto} in a mixture model results in a nonparametric estimator that can adapt to multimodality, asymmetry, and heavy tails. A conditional density estimator is built by modeling the parameters of the mixture estimator as functions of X. We use a neural network to implement these functions. Such conditional density estimators have important applications in many domains such as finance and insurance. We show experimentally that this novel approach better models the conditional density in terms of likelihood, compared to competing algorithms: conditional mixture models with other types of components and a classical kernel-based nonparametric model.}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1455 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1456
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1457 @ARTICLE{Carreau+Bengio-extreme-2009,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1458 author = {Carreau, Julie and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1459 title = {A Hybrid {Pareto} Model for Asymmetric Fat-Tailed Data: the univariate case},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1460 journal = {Extremes},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1461 volume = {12},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1462 number = {1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1463 year = {2009},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1464 pages = {53--76},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1465 abstract = {Density estimators that can adapt to asymmetric heavy tails are required in many applications such as finance and insurance. Extreme Value Theory (EVT) has developped principled methods based on asymptotic results to estimate the tails of most distributions. However, the finite sample approximation might introduce a severe bias in many cases. Moreover, the full range of the distribution is often needed, not only the tail area. On the other hand, non-parametric methods, while being powerful where data are abundant, fail to extrapolate properly in the tail area. We put forward a non-parametric density estimator that brings together the strengths of non-parametric density estimation and of EVT. A hybrid {Pareto} distribution that can be used in a mixture model is proposed to extend the generalized {Pareto} (GP) to the whole real axis. Experiments on simulated data show the following. On one hand, the mixture of hybrid {Pareto}s converges faster in terms of log-likelihood and provides good estimates of the tail of the distributions when compared with other density estimators including the GP distribution. On the other hand, the mixture of hybrid {Pareto}s offers an alternate way to estimate the tail index which is comparable to the one estimated with the standard GP methodology. The mixture of hybrids is also evaluated on the Danish fire insurance data set.}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1466 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1467
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1468 @PHDTHESIS{Carreau-PhD-2007,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1469 author = {Carreau, Julie},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1470 keywords = {density estimation, extreme values, generalized {Pareto} distribution, heavy-tailed distribution, mixture of distributions, neural networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1471 title = {Mod{\`{e}}les {Pareto} hybrides pour distributions asym{\'{e}}triques et {\`{a}} queues lourdes},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1472 year = {2007},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1473 school = {UdeM},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1474 abstract = {We put forward a class of density estimators that can adapt to asymmetric, multi-modal and heavy-tailed distributions. Such distributions occur in many application domains such as finance and insurance. Mixture of gaussians are flexible non-parametric density estimators that have good approximation properties when the number of components is well chosen with respect to the training set size. However, those models are performing poorly on heavy-tailed data because few observations occur in the tail area. To solve this problem, we resort to extreme value theory where methods based on sound parametric assumptions have been developped to enable extrapolation beyond the range of the observations. More precisely, we build on the PoT method that was developped in hydrology where PoT stands for "Peaks-over-Threshold". The observations exceeding a given threshold are modeled by the generalized {Pareto} distribution. This distribution can approximate arbitrarily well the tail of most distributions. We build a new distribution, the hybrid {Pareto}, by stitching together a truncated Normal and a generalized {Pareto} distribution. We impose continuity constraints at the junction point. The hybrid {Pareto} is thus a smooth distribution that can be used in a mixture model. The behavior of the upper tail of the hybrid is similar to the behavior of the generalized {Pareto} tail. Moreover, the threshold inherent in the the PoT methodology can now be defined implicitly as the junction point of the component with the heaviest tail. This component also determines the tail index of the mixture. Hence, the hybrid {Pareto} mixture offers an alternate way to estimate the tail index associated with heavy-tailed data. In several applications, information that has predictive power on the variable of interest is available. In that case, we want to model the conditional density of Y given X, the vector containing predictive information. When the distribution of Y given X is asymmetric, multi-modal and heavy-tailed, we propose to use a mixure of hybrid {Pareto}s whose parameters are functions of X. Those functions are implemented by means of a neural network with one hidden layer. Neural neworks are non-parametric models that can, in principle, approximate any continuous function. Experiments on artificial and real data sets show that the hybrid {Pareto} mixture, unconditional and conditional, outperforms other density estimators in terms of log-likelihood.}
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1475 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1476
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1477 @INPROCEEDINGS{casagrande+eck+kegl:icmc2005,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1478 author = {Casagrande, Norman and Eck, Douglas and K{\'{e}}gl, Bal{\'{a}}zs},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1479 title = {Geometry in Sound: A Speech/Music Audio Classifier Inspired by an Image Classifier},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1480 booktitle = {{Proceedings of the International Computer Music Conference (ICMC)}},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1481 year = {2005},
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fsavard
parents:
diff changeset
1482 pages = {207--210},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1483 url = {http://www.iro.umontreal.ca/~eckdoug/papers/2005_icmc_casagrande.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1484 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1485 sourcetype={Conference},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1486 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1487
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1488 @INPROCEEDINGS{casagrande+eck+kegl:ismir2005,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1489 author = {Casagrande, Norman and Eck, Douglas and K{\'{e}}gl, Bal{\'{a}}zs},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1490 title = {Frame-Level Audio Feature Extraction using {A}da{B}oost},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1491 booktitle = {{Proceedings of the 6th International Conference on Music Information Retrieval ({ISMIR} 2005)}},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1492 year = {2005},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1493 pages = {345--350},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1494 url = {http://www.iro.umontreal.ca/~eckdoug/papers/2005_ismir_casagrande.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1495 source={OwnPublication},
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fsavard
parents:
diff changeset
1496 sourcetype={Conference},
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fsavard
parents:
diff changeset
1497 }
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fsavard
parents:
diff changeset
1498
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1499 @PROCEEDINGS{ccai2006,
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parents:
diff changeset
1500 editor = {Lamontagne, Luc and Marchand, Mario},
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parents:
diff changeset
1501 title = {Advances in Artificial Intelligence, 19th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2006, Qu{\'{e}}bec City, Qu{\'{e}}bec, Canada, June 7-9, 2006, Proceedings},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1502 booktitle = {Canadian Conference on AI},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1503 series = {Lecture Notes in Computer Science},
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fsavard
parents:
diff changeset
1504 volume = {4013},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1505 year = {2006},
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fsavard
parents:
diff changeset
1506 publisher = {Springer}
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fsavard
parents:
diff changeset
1507 }
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fsavard
parents:
diff changeset
1508
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1509 @INPROCEEDINGS{Chapados+Bengio-2006,
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fsavard
parents:
diff changeset
1510 author = {Chapados, Nicolas and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1511 title = {The K Best-Paths Approach to Approximate Dynamic Programming with Application to Portfolio Optimization},
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fsavard
parents:
diff changeset
1512 booktitle = {AI06},
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fsavard
parents:
diff changeset
1513 year = {2006},
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fsavard
parents:
diff changeset
1514 pages = {491-502}
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fsavard
parents:
diff changeset
1515 }
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fsavard
parents:
diff changeset
1516
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fsavard
parents:
diff changeset
1517 @INPROCEEDINGS{Chapados+Bengio-2007,
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fsavard
parents:
diff changeset
1518 author = {Chapados, Nicolas and Bengio, Yoshua},
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parents:
diff changeset
1519 title = {Forecasting Commodity Contract Spreads with Gaussian Process},
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fsavard
parents:
diff changeset
1520 booktitle = {13th Intarnational Conference on Computing in Economics and Finance},
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parents:
diff changeset
1521 year = {2007},
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parents:
diff changeset
1522 abstract = {We introduce a functional representation of time series which allows forecasts to be performed over an unspecified horizon with progressively-revealed information sets. By virtue of using Gaussian processes, a complete covariance matrix between forecasts at several time-steps is available. This information is put to use in an application to actively trade price spreads between commodity futures contracts. The approach delivers impressive out-of-sample risk-adjusted returns after transaction costs on a portfolio of 30 spreads.}
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parents:
diff changeset
1523 }
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fsavard
parents:
diff changeset
1524
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1525 @ARTICLE{Chapados+Bengio-2008-JOC,
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fsavard
parents:
diff changeset
1526 author = {Chapados, Nicolas and Bengio, Yoshua},
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fsavard
parents:
diff changeset
1527 title = {Noisy K Best-Paths for Approximate Dynamic Programming with Application to Portfolio Optimization},
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fsavard
parents:
diff changeset
1528 journal = {Journal of Computers},
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parents:
diff changeset
1529 volume = {2},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1530 number = {1},
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fsavard
parents:
diff changeset
1531 year = {2007},
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parents:
diff changeset
1532 pages = {12--19},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1533 abstract = {We describe a general method to transform a non-Markovian sequential decision problem into a supervised learning problem using a K-bestpaths algorithm. We consider an application in financial portfolio management where we can train a controller to directly optimize a Sharpe Ratio (or other risk-averse non-additive) utility function. We illustrate the approach by demonstrating experimental results using a kernel-based controller architecture that would not normally be considered in traditional
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1534 reinforcement learning or approximate dynamic programming.We further show that using a non-additive criterion (incremental Sharpe Ratio) yields a noisy K-best-paths extraction problem, that can give substantially improved performance.}
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parents:
diff changeset
1535 }
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parents:
diff changeset
1536
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1537 @MASTERSTHESIS{Chapados-Msc-2000,
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parents:
diff changeset
1538 author = {Chapados, Nicolas},
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fsavard
parents:
diff changeset
1539 title = {Crit{\`{e}}res d'optimisation d'algorithmes d'apprentissage en gestion de portefeuille},
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parents:
diff changeset
1540 year = {2000},
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parents:
diff changeset
1541 school = {Universit{\'{e}} de Montr{\'{e}}al}
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fsavard
parents:
diff changeset
1542 }
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fsavard
parents:
diff changeset
1543
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fsavard
parents:
diff changeset
1544 @INPROCEEDINGS{chapados2000,
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parents:
diff changeset
1545 author = {Chapados, Nicolas and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1546 title = {Cost Functions and Model Combination for {VaR}-Based Asset Allocation Using Neural Networks},
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parents:
diff changeset
1547 booktitle = {Computational Finance 2000},
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fsavard
parents:
diff changeset
1548 year = {2000},
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parents:
diff changeset
1549 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/compfin2000_final.pdf},
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parents:
diff changeset
1550 abstract = {We introduce an asset-allocation framework based on the active control of the value-at-risk of the portfolio. Within this framework, we compare two paradigms for making the allocation using neural networks. The first one uses the network to make a forecast of asset behavior, in conjunction with a traditional mean-variance allocator for constructing the portfolio. The second paradigm uses the network to directly make the portfolio allocation decisions. We consider a method for performing soft input variable selection, and show its considerable utility. We use model combination (committee) methods to systematize the choice of hyperparemeters during training. We show that committees using both paradigms are significantly outperforming the benchmark market performance.},
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parents:
diff changeset
1551 topics={Finance},cat={C},
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fsavard
parents:
diff changeset
1552 }
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fsavard
parents:
diff changeset
1553
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1554 @ARTICLE{chapados:2001,
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fsavard
parents:
diff changeset
1555 author = {Chapados, Nicolas and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1556 title = {Cost Functions and Model Combination for VaR--based Asset Allocation using Neural Networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1557 journal = {IEEE Transactions on Neural Networks},
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fsavard
parents:
diff changeset
1558 volume = {12},
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fsavard
parents:
diff changeset
1559 number = {4},
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parents:
diff changeset
1560 year = {2001},
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parents:
diff changeset
1561 pages = {890--906},
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fsavard
parents:
diff changeset
1562 abstract = {We introduce an asset-allocation framework based on the active control of the value-at-risk of the portfolio. Within this framework, we
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1563 compare two paradigms for making the allocation using neural networks. The first one uses the network to make a forecast of asset behavior, in conjunction with a traditional mean-variance allocator for constructing the portfolio. The second paradigm uses the network to directly make the portfolio allocation decisions. We consider a method for performing soft input variable selection, and show its considerable utility. We use model combination (committee) methods to systematize the choice of hyperparemeters during training. We show that committees
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1564 using both paradigms are significantly outperforming the benchmark market performance.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1565 topics={Finance},cat={J},
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parents:
diff changeset
1566 }
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parents:
diff changeset
1567
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1568 @ARTICLE{chapados:2003,
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parents:
diff changeset
1569 author = {Bengio, Yoshua and Chapados, Nicolas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1570 title = {Extensions to Metric-Based Model Selection},
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1571 year = {2003},
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parents:
diff changeset
1572 journal = {Journal of Machine Learning Research},
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fsavard
parents:
diff changeset
1573 abstract = {Metric-based methods have recently been introduced for model selection and regularization, often yielding very significant improvements over the alternatives tried (including cross-validation). All these methods require unlabeled data over which to compare functions and detect gross differences in behavior away from the training points. We introduce three new extensions of the metric model selection methods and apply them to feature selection. The first extension takes advantage of the particular case of time-series data in which the task involves prediction with a horizon h. The idea is to use at t the h unlabeled examples that precede t for model selection. The second extension takes advantage of the different error distributions of cross-validation and the metric methods: cross-validation tends to have a larger variance and is unbiased. A hybrid combining the two model selection methods is rarely beaten by any of the two methods. The third extension deals with the case when unlabeled data is not available at all, using an estimated input density. Experiments are described to study these extensions in the context of capacity control and feature subset selection.},
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1574 topics={ModelSelection,Finance},cat={J},
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parents:
diff changeset
1575 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1576
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1577 @ARTICLE{chapelle:2001,
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parents:
diff changeset
1578 author = {Chapelle, Olivier and Vapnik, Vladimir and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1579 title = {Model Selection for Small Sample Regression},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1580 journal = {Machine Learning},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1581 year = {2001},
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1582 abstract = {Model selection is an important ingredient of many machine learning algorithms, in particular when the sample size in small, in order to strike the right trade-off between overfitting and underfitting. Previous classical results for linear regression are based on an asymptotic analysis. We present a new penalization method for performing model selection for regression that is appropriate even for small samples. Our penalization is based on an accurate estimator of the ratio of the expected training error and the expected generalization error, in terms of the expected eigenvalues of the input covariance matrix.},
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parents:
diff changeset
1583 topics={ModelSelection},cat={J},
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fsavard
parents:
diff changeset
1584 }
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fsavard
parents:
diff changeset
1585
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fsavard
parents:
diff changeset
1586 @INCOLLECTION{chapter-eval-longterm-2001,
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parents:
diff changeset
1587 author = {Schmidhuber, Juergen and Hochreiter, Sepp and Bengio, Yoshua},
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parents:
diff changeset
1588 editor = {Kolen, J. and Kremer, S.},
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fsavard
parents:
diff changeset
1589 title = {Evaluating Benchmark Problems by Random Guessing},
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fsavard
parents:
diff changeset
1590 booktitle = {Field Guide to Dynamical Recurrent Networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1591 year = {2001},
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fsavard
parents:
diff changeset
1592 publisher = {IEEE Press},
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fsavard
parents:
diff changeset
1593 topics={LongTerm},cat={B},
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fsavard
parents:
diff changeset
1594 }
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fsavard
parents:
diff changeset
1595
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fsavard
parents:
diff changeset
1596 @INCOLLECTION{chapter-gradient-document-2001,
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parents:
diff changeset
1597 author = {{LeCun}, Yann and Bottou, {L{\'{e}}on} and Bengio, Yoshua and Haffner, Patrick},
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parents:
diff changeset
1598 editor = {Haykin, S. and Kosko, B.},
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parents:
diff changeset
1599 title = {Gradient-Based Learning Applied to Document Recognition},
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parents:
diff changeset
1600 booktitle = {Intelligent Signal Processing},
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parents:
diff changeset
1601 year = {2001},
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parents:
diff changeset
1602 pages = {306--351},
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fsavard
parents:
diff changeset
1603 publisher = {IEEE Press},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1604 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/lecun-01a.pdf},
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parents:
diff changeset
1605 abstract = {Multilayer Neural Networks trained with a backprppagation algorithm constitute the best example of a successful Gradient-Based Learning technique. Given an appropriate network architecture, Gradient-Based Learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional Neural Networks, that are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques.
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parents:
diff changeset
1606 Real-life document recognition systems are composed of multiple modules including field extraction, segmentation, recognition, and language modeling. A new learning paradigm, called Graph Transformer Networks (GTN), allows such multi-module systems to be trained globally using Gradient-Based methods so as to monimize an overall peformance measure.
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parents:
diff changeset
1607 Two systems for on-line handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of Graph Transformer Networks.
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parents:
diff changeset
1608 A Graph Transformer Network for reading bank check is also described. It uses Convolutional Neural Network character recognizers combined with a global training technique to provides record accuracy on business and personal checks. It is deployed commercially and reads several million checks per day.},
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parents:
diff changeset
1609 topics={PriorKnowledge,Speech},cat={B},
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parents:
diff changeset
1610 }
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fsavard
parents:
diff changeset
1611
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1612 @INCOLLECTION{chapter-gradient-flow-2001,
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parents:
diff changeset
1613 author = {Hochreiter, Sepp and Bengio, Yoshua and Frasconi, Paolo},
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parents:
diff changeset
1614 editor = {Kolen, J. and Kremer, S.},
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parents:
diff changeset
1615 title = {Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies},
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parents:
diff changeset
1616 booktitle = {Field Guide to Dynamical Recurrent Networks},
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parents:
diff changeset
1617 year = {2001},
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parents:
diff changeset
1618 publisher = {IEEE Press},
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fsavard
parents:
diff changeset
1619 topics={LongTerm},cat={B},
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fsavard
parents:
diff changeset
1620 }
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fsavard
parents:
diff changeset
1621
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1622 @INPROCEEDINGS{chemero+eck:1999,
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parents:
diff changeset
1623 author = {Chemero, T. and Eck, Douglas},
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parents:
diff changeset
1624 title = {An Exploration of Representational Complexity via Coupled Oscillators},
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fsavard
parents:
diff changeset
1625 booktitle = {{Proceedings of the Tenth Midwest Artificial Intelligence and Cognitive Science Society}},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1626 year = {1999},
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parents:
diff changeset
1627 publisher = {MIT Press},
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parents:
diff changeset
1628 url = {http://www.iro.umontreal.ca/~eckdoug/papers/1999_chemero.pdf},
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parents:
diff changeset
1629 abstract = {We note some inconsistencies in a view of representation which takes {\it decoupling} to be of key importance. We explore these inconsistencies using examples of representational vehicles taken from coupled oscillator theory and suggest a new way to reconcile {\it coupling} with {\it absence}. Finally, we tie these views to a teleological definition of representation.},
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parents:
diff changeset
1630 source={OwnPublication},
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parents:
diff changeset
1631 sourcetype={Conference},
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parents:
diff changeset
1632 }
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parents:
diff changeset
1633
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1634 @ARTICLE{ChemInfModel2006,
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parents:
diff changeset
1635 author = {Erhan, Dumitru and {L'Heureux}, Pierre-Jean and Yue, Shi Yi and Bengio, Yoshua},
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parents:
diff changeset
1636 title = {Collaborative Filtering on a Family of Biological Targets},
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parents:
diff changeset
1637 journal = {J. Chem. Inf. Model.},
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parents:
diff changeset
1638 volume = {46},
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parents:
diff changeset
1639 number = {2},
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parents:
diff changeset
1640 year = {2006},
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parents:
diff changeset
1641 pages = {626--635},
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parents:
diff changeset
1642 abstract = {Building a QSAR model of a new biological target for which few screening data are available is a statistical
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fsavard
parents:
diff changeset
1643 challenge. However, the new target may be part of a bigger family, for which we have more screening data.
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parents:
diff changeset
1644 Collaborative filtering or, more generally, multi-task learning, is a machine learning approach that improves
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1645 the generalization performance of an algorithm by using information from related tasks as an inductive
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1646 bias. We use collaborative filtering techniques for building predictive models that link multiple targets to
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1647 multiple examples. The more commonalities between the targets, the better the multi-target model that can
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1648 be built. We show an example of a multi-target neural network that can use family information to produce
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1649 a predictive model of an undersampled target. We evaluate JRank, a kernel-based method designed for
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1650 collaborative filtering. We show their performance on compound prioritization for an HTS campaign and
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fsavard
parents:
diff changeset
1651 the underlying shared representation between targets. JRank outperformed the neural network both in the
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fsavard
parents:
diff changeset
1652 single- and multi-target models.},
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fsavard
parents:
diff changeset
1653 topics={Bioinformatic,MultiTask},cat={J},
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fsavard
parents:
diff changeset
1654 }
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fsavard
parents:
diff changeset
1655
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1656 @TECHREPORT{collobert:2001:rr01-12,
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fsavard
parents:
diff changeset
1657 author = {Collobert, Ronan and Bengio, Samy and Bengio, Yoshua},
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fsavard
parents:
diff changeset
1658 title = {A Parallel Mixture of {SVM}s for Very Large Scale Problems},
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fsavard
parents:
diff changeset
1659 number = {12},
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parents:
diff changeset
1660 year = {2001},
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parents:
diff changeset
1661 institution = {IDIAP},
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fsavard
parents:
diff changeset
1662 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/IDIAP-RR-01-12.ps},
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parents:
diff changeset
1663 abstract = {Support Vector Machines ({SVM}s) are currently the state-of-the-art models for many classification problems but they suffer from the complexity of their training algorithm which is at least quadratic with respect to the number of examples. Hence, it is hopeless to try to solve real-life problems having more than a few hundreds of thousands examples with {SVM}s. The present paper proposes a new mixture of {SVM}s that can be easily implemented in parallel and where each {SVM} is trained on a small subset of the whole dataset. Experiments on a large benchmark dataset (Forest) yielded significant time improvement (time complexity appears empirically to locally grow linearly with the number of examples). In addition, and that is a surprise, a significant improvement in generalization was observed.},
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1664 topics={Kernel},cat={T},
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parents:
diff changeset
1665 }
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parents:
diff changeset
1666
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1667 @ARTICLE{collobert:2002,
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parents:
diff changeset
1668 author = {Collobert, Ronan and Bengio, Samy and Bengio, Yoshua},
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fsavard
parents:
diff changeset
1669 title = {Parallel Mixture of {SVM}s for Very Large Scale Problem},
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parents:
diff changeset
1670 journal = {Neural Computation},
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parents:
diff changeset
1671 year = {2002},
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fsavard
parents:
diff changeset
1672 abstract = {Support Vector Machines ({SVM}s) are currently the state-of-the-art models for many classification problems but they suffer from the complexity of their training algorithm which is at least quadratic with respect to the number of examples. Hence, it is hopeless to try to solve real-life problems having more than a few hundreds of thousands examples with {SVM}s. The present paper proposes a new mixture of {SVM}s that can be easily implemented in parallel and where each {SVM} is trained on a small subset of the whole dataset. Experiments on a large benchmark dataset (Forest) yielded significant time improvement (time complexity appears empirically to locally grow linearly with the number of examples). In addition, and that is a surprise, a significant improvement in generalization was observed.},
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1673 topics={HighDimensional,Kernel},cat={J},
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fsavard
parents:
diff changeset
1674 }
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parents:
diff changeset
1675
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1676 @BOOK{collobert:2002:book,
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parents:
diff changeset
1677 author = {Collobert, Ronan and Bengio, Yoshua and Bengio, Samy},
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parents:
diff changeset
1678 editor = {Lee, S. W. and Verri, A.},
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parents:
diff changeset
1679 title = {Scaling Large Learning Problems with Hard Parallel Mixtures},
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fsavard
parents:
diff changeset
1680 booktitle = {Pattern Recognition with Support Vector Machines},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1681 series = {Lecture Notes in Computer Science},
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fsavard
parents:
diff changeset
1682 volume = {2388},
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parents:
diff changeset
1683 year = {2002},
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parents:
diff changeset
1684 publisher = {Springer-Verlag},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1685 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/2002_mixtures_svm.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1686 abstract = {A challenge for statistical learning is to deal with large data sets, e.g. in data mining. Popular learning algorithms such as Support Vector Machines have training time at least quadratic in the number of examples: they are hopeless to solve prolems with a million examples. We propose a "hard parallelizable mixture" methodology which yields significantly reduced training time through modularization and parallelization: the training data is iteratively partitioned by a "gater" model in such a way that it becoms easy to learn an "expert" model separately in each region of the parition. A probabilistic extension and the use of a set of generative models allows representing a gater so that all pieces of the model are locally trained. For {SVM}s, time complexity appears empirically to locally grow linearly with the number of examples, while generalization performance can be enhanced. For the probabilistic version of the algorithm, the iterative algorithm provably goes down in a cost function that is an upper bound on the negative log-likelihood.},
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parents:
diff changeset
1687 topics={Kernel},cat={B},
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fsavard
parents:
diff changeset
1688 }
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fsavard
parents:
diff changeset
1689
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1690 @MISC{copyright-CTAI,
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fsavard
parents:
diff changeset
1691 author = {Bengio, Yoshua and Ducharme, R{\'{e}}jean and Dorion, Christian},
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parents:
diff changeset
1692 title = {Commodity Trading Advisor Index},
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fsavard
parents:
diff changeset
1693 year = {2004-2009},
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fsavard
parents:
diff changeset
1694 howpublished = {copyright, and commercialized software license.}
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fsavard
parents:
diff changeset
1695 }
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fsavard
parents:
diff changeset
1696
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1697 @MISC{copyright-PLearn,
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fsavard
parents:
diff changeset
1698 author = {Vincent, Pascal and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1699 title = {{PLearn}, a {C++} Machine Learning Library},
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fsavard
parents:
diff changeset
1700 year = {1998-2009},
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fsavard
parents:
diff changeset
1701 howpublished = {copyright, public domain license.},
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fsavard
parents:
diff changeset
1702 url = {www.plearn.org}
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fsavard
parents:
diff changeset
1703 }
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fsavard
parents:
diff changeset
1704
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1705 @ARTICLE{Cosi90,
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fsavard
parents:
diff changeset
1706 author = {Cosi, Piero and Bengio, Yoshua and De Mori, Renato},
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fsavard
parents:
diff changeset
1707 title = {Phonetically-based multi-layered networks for acoustic property extraction and automatic speech recognition},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1708 journal = {Speech Communication},
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fsavard
parents:
diff changeset
1709 volume = {9},
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fsavard
parents:
diff changeset
1710 number = {1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1711 year = {1990},
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fsavard
parents:
diff changeset
1712 pages = {15--30},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1713 topics={PriorKnowledge,Speech},cat={J},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1714 }
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fsavard
parents:
diff changeset
1715
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1716 @INCOLLECTION{courville+eck+bengio:nips2009,
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fsavard
parents:
diff changeset
1717 author = {Courville, Aaron and Eck, Douglas and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1718 editor = {},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1719 title = {An Infinite Factor Model Hierarchy Via a Noisy-Or Mechanism},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1720 booktitle = {Neural Information Processing Systems Conference (NIPS) 22},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1721 year = {2009},
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fsavard
parents:
diff changeset
1722 pages = {405--413},
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fsavard
parents:
diff changeset
1723 publisher = {},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1724 url = {http://books.nips.cc/papers/files/nips22/NIPS2009_1100.pdf},
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fsavard
parents:
diff changeset
1725 source={OwnPublication},
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fsavard
parents:
diff changeset
1726 sourcetype={Conference},
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fsavard
parents:
diff changeset
1727 pdf={""},
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fsavard
parents:
diff changeset
1728 }
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fsavard
parents:
diff changeset
1729
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fsavard
parents:
diff changeset
1730 @INPROCEEDINGS{davies+plumbley+eck:waspaa2009,
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parents:
diff changeset
1731 author = {Davies, M. and Plumbley, M. and Eck, Douglas},
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fsavard
parents:
diff changeset
1732 title = {Towards a musical beat emphasis function},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1733 booktitle = {Proceedings of IEEE WASPAA},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1734 year = {2009},
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fsavard
parents:
diff changeset
1735 organization = {IEEE Workshop on Applications of Signal Processing to Audio and Acoustics},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1736 source={OwnPublication},
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fsavard
parents:
diff changeset
1737 sourcetype={Conference},
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fsavard
parents:
diff changeset
1738 }
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fsavard
parents:
diff changeset
1739
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1740 @INPROCEEDINGS{Delalleau+al-2005,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1741 author = {Delalleau, Olivier and Bengio, Yoshua and Le Roux, Nicolas},
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1742 editor = {Cowell, Robert G. and Ghahramani, Zoubin},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1743 title = {Efficient Non-Parametric Function Induction in Semi-Supervised Learning},
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fsavard
parents:
diff changeset
1744 booktitle = {Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics (AISTATS'05)},
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fsavard
parents:
diff changeset
1745 year = {2005},
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fsavard
parents:
diff changeset
1746 pages = {96--103},
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fsavard
parents:
diff changeset
1747 publisher = {Society for Artificial Intelligence and Statistics},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1748 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/semisup_aistats2005.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1749 abstract = {There has been an increase of interest for semi-supervised learning recently, because of the many datasets with large amounts of unlabeled examples and only a few labeled ones. This paper follows up on proposed nonparametric algorithms which provide an estimated continuous label for the given unlabeled examples. First, it extends them to function induction algorithms that minimize a regularization criterion applied to an out-of-sample example, and happen to have the form of Parzen windows regressors. This allows to predict test labels without solving again a linear system of dimension n (the number of unlabeled and labeled training examples), which can cost O(n^3). Second, this function induction procedure gives rise to an efficient approximation of the training process, reducing the linear system to be solved to m << n unknowns, using only a subset of m examples. An improvement of O(n^2/m^2) in time can thus be obtained. Comparative experiments are presented, showing the good performance of the induction formula and approximation algorithm.},
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1750 topics={Unsupervised},cat={C},
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fsavard
parents:
diff changeset
1751 }
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fsavard
parents:
diff changeset
1752
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1753 @INCOLLECTION{Delalleau+al-ssl-2006,
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1754 author = {Delalleau, Olivier and Bengio, Yoshua and Le Roux, Nicolas},
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parents:
diff changeset
1755 editor = {Chapelle, Olivier and {Sch{\"{o}}lkopf}, Bernhard and Zien, Alexander},
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parents:
diff changeset
1756 title = {Large-Scale Algorithms},
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fsavard
parents:
diff changeset
1757 booktitle = {Semi-Supervised Learning},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1758 year = {2006},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1759 pages = {333--341},
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fsavard
parents:
diff changeset
1760 publisher = {{MIT} Press},
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fsavard
parents:
diff changeset
1761 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/delalleau_ssl.pdf},
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fsavard
parents:
diff changeset
1762 abstract = {In Chapter 11, it is shown how a number of graph-based semi-supervised learning
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1763 algorithms can be seen as the minimization of a specific cost function, leading to a
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1764 linear system with n equations and unknowns (with n the total number of labeled
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1765 and unlabeled examples). Solving such a linear system will in general require on the
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1766 order of O(kn2) time and O(kn) memory (for a sparse graph where each data point
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1767 has k neighbors), which can be prohibitive on large datasets (especially if k = n,
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fsavard
parents:
diff changeset
1768 i.e. the graph is dense). We present in this chapter a subset selection method that
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1769 can be used to reduce the original system to one of size m << n. The idea is to solve
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1770 for the labels of a subset S of X of only m points, while still retaining information
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1771 from the rest of the data by approximating their label with a linear combination of
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1772 the labels in S (using the induction formula presented in Chapter 11). This leads
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fsavard
parents:
diff changeset
1773 to an algorithm whose computational requirements scale as O(m2n) and memory
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1774 requirements as O(m2), thus allowing one to take advantage of significantly bigger
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fsavard
parents:
diff changeset
1775 unlabeled datasets than with the original algorithms.},
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parents:
diff changeset
1776 cat={B},topics={Unsupervised},
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fsavard
parents:
diff changeset
1777 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1778
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fsavard
parents:
diff changeset
1779 @INCOLLECTION{DeMori90a,
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fsavard
parents:
diff changeset
1780 author = {De Mori, Renato and Bengio, Yoshua and Cosi, Piero},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1781 editor = {Mohr, R. and Pavlidis, T. and Sanfelin, A.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1782 title = {On the use of an ear model and multi-layer networks for automatic speech recognition},
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fsavard
parents:
diff changeset
1783 booktitle = {Structural Pattern Analysis},
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fsavard
parents:
diff changeset
1784 year = {1990},
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fsavard
parents:
diff changeset
1785 publisher = {World Scientific},
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fsavard
parents:
diff changeset
1786 topics={PriorKnowledge,Speech},cat={B},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1787 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1788
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1789 @INPROCEEDINGS{Desjardins+al-2010,
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fsavard
parents:
diff changeset
1790 author = {Desjardins, Guillaume and Courville, Aaron and Bengio, Yoshua},
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parents:
diff changeset
1791 title = {Tempered {Markov} Chain Monte Carlo for training of Restricted {Boltzmann} Machine},
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fsavard
parents:
diff changeset
1792 booktitle = {Proceedings of AISTATS 2010},
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fsavard
parents:
diff changeset
1793 volume = {9},
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parents:
diff changeset
1794 year = {2010},
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parents:
diff changeset
1795 pages = {145-152},
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fsavard
parents:
diff changeset
1796 abstract = {Alternating Gibbs sampling is the most common scheme used for sampling from Restricted {Boltzmann} Machines (RBM), a crucial component in deep architectures such as Deep Belief Networks. However, we find that it often does a very poor job of rendering the diversity of modes captured by the trained model. We suspect that this hinders the advantage that could in principle be brought by training algorithms relying on Gibbs sampling for uncovering spurious modes, such as the Persistent Contrastive Divergence algorithm. To alleviate this problem, we explore the use of tempered {Markov} Chain Monte-Carlo for sampling in RBMs. We find both through visualization of samples and measures of likelihood on a toy dataset that it helps both sampling and learning.}
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parents:
diff changeset
1797 }
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fsavard
parents:
diff changeset
1798
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parents:
diff changeset
1799 @TECHREPORT{Desjardins-2008,
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parents:
diff changeset
1800 author = {Desjardins, Guillaume and Bengio, Yoshua},
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fsavard
parents:
diff changeset
1801 keywords = {Convolutional Architectures, Deep Networks, RBM, Vision},
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fsavard
parents:
diff changeset
1802 title = {Empirical Evaluation of Convolutional RBMs for Vision},
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fsavard
parents:
diff changeset
1803 number = {1327},
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fsavard
parents:
diff changeset
1804 year = {2008},
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fsavard
parents:
diff changeset
1805 institution = {D{\'{e}}partement d'Informatique et de Recherche Op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
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fsavard
parents:
diff changeset
1806 abstract = {Convolutional Neural Networks ({CNN}) have had great success in machine learning tasks involving vision and represent one of the early successes of deep networks. Local receptive fields and weight
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1807 sharing make their architecture ideally suited for vision tasks by helping to enforce a prior based on our knowledge of natural images. This same prior could also be applied to recent developments in the field of deep networks, in order to tailor these new architectures for artificial vision. In this context, we show how the Restricted {Boltzmann} Machine (RBM), the building block of Deep Belief Networks (DBN), can be adapted to operate in a convolutional manner. We compare their performance to standard fully-connected RBMs on a simple visual learning task and show that the convolutional RBMs (CRBMs) converge to smaller values of the negative likelihood function. Our experiments also indicate that CRBMs are more efficient than standard RBMs trained on small image patches, with the CRBMs having faster convergence.}
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parents:
diff changeset
1808 }
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fsavard
parents:
diff changeset
1809
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1810 @TECHREPORT{Desjardins-tech-2009,
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fsavard
parents:
diff changeset
1811 author = {Desjardins, Guillaume and Courville, Aaron and Bengio, Yoshua and Vincent, Pascal and Delalleau, Olivier},
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fsavard
parents:
diff changeset
1812 keywords = {CD, PCD, RBM, simulated tempering, tempered MCMC, unsupervised learning},
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fsavard
parents:
diff changeset
1813 title = {Tempered {Markov} Chain Monte Carlo for training of Restricted {Boltzmann} Machines},
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fsavard
parents:
diff changeset
1814 number = {1345},
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fsavard
parents:
diff changeset
1815 year = {2009},
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fsavard
parents:
diff changeset
1816 institution = {D{\'{e}}partement d'Informatique et de Recherche Op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1817 abstract = {Alternating Gibbs sampling is the most common scheme used for sampling from Restricted {Boltzmann} Machines (RBM), a crucial component in deep architectures such as Deep Belief Networks. However, we find that it often does a very poor job of rendering the diversity of modes captured by the trained model. We suspect that this hinders the advantage that could in principle be brought by training algorithms relying on Gibbs sampling for uncovering spurious modes, such as the Persistent Contrastive Divergence algorithm. To alleviate this problem, we
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1818 explore the use of tempered {Markov} Chain Monte-Carlo for sampling in RBMs. We find both through visualization of samples and measures of likelihood that it helps both sampling and learning.}
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parents:
diff changeset
1819 }
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parents:
diff changeset
1820
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1821 @ARTICLE{Dugas+Bengio-2009,
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parents:
diff changeset
1822 author = {Dugas, Charles and Bengio, Yoshua and Belisle, Francois and Nadeau, Claude and Garcia, Rene},
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parents:
diff changeset
1823 title = {Incorporating Functional Knowledge in Neural Networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1824 journal = {The Journal of Machine Learning Research},
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fsavard
parents:
diff changeset
1825 volume = {10},
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1826 year = {2009},
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parents:
diff changeset
1827 pages = {1239--1262},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1828 abstract = {Incorporating prior knowledge of a particular task into the architecture of a learning algorithm can greatly improve generalization performance. We study here a case where we know that the function to be learned is non-decreasing in its two arguments and convex in one of them. For this purpose we propose a class of functions similar to multi-layer neural networks but (1) that has those properties, (2) is a universal approximator of Lipschitz functions with these and other properties. We apply this new class of functions to the task of modelling the price of call options. Experiments show improvements on regressing the price of call options using the new types of function classes that incorporate the a priori constraints.}
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parents:
diff changeset
1829 }
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fsavard
parents:
diff changeset
1830
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1831 @PHDTHESIS{Dugas-Phd-2003,
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fsavard
parents:
diff changeset
1832 author = {Dugas, Charles},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1833 title = {Les algorithmes d'apprentissage appliqu{\'{e}}s aux risques financiers},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1834 year = {2003},
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fsavard
parents:
diff changeset
1835 school = {Universit{\'{e}} de Montr{\'{e}}al}
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fsavard
parents:
diff changeset
1836 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1837
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1838 @ARTICLE{dugas:2003,
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fsavard
parents:
diff changeset
1839 author = {Dugas, Charles and Bengio, Yoshua and Chapados, Nicolas and Vincent, Pascal and Denoncourt, Germain and Fournier, Christian},
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fsavard
parents:
diff changeset
1840 title = {Statistical Learning Algorithms Applied to Automobile Insurance Ratemaking},
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fsavard
parents:
diff changeset
1841 journal = {CAS Forum},
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fsavard
parents:
diff changeset
1842 volume = {1},
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fsavard
parents:
diff changeset
1843 number = {1},
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parents:
diff changeset
1844 year = {2003},
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parents:
diff changeset
1845 pages = {179--214},
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fsavard
parents:
diff changeset
1846 abstract = {We recently conducted a research project for a large North American automobile insurer. This study was the most exhaustive ever undertaken by this particular insurer and lasted over an entire year. We analyzed the discriminating power of each variable used for ratemaking. We analyzed the performance of several models within five broad categories: linear regressions, generalized linear models, decision trees, neural networks and support vector machines. In this paper, we present the main results of this study. We qualitatively compare models and show how neural networks can represent high-order nonlinear dependencies with a small number of parameters, each of which is estimated on a large proportion of the data, thus yielding low variance. We thoroughly explain the purpose of the nonlinear sigmoidal transforms which are at the very heart of neural networks' performances. The main numerical result is a statistically significant reduction in the out-of-sample mean-squared error using the neural network model and our ability to substantially reduce the median premium by charging more to the highest risks. This in turn can translate into substantial savings and financial benefits for an insurer. We hope this paper goes a long way towards convincing actuaries to include neural networks within their set of modeling tools for ratemaking.},
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1847 topics={Finance,Mining},cat={J},
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parents:
diff changeset
1848 }
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fsavard
parents:
diff changeset
1849
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1850 @INPROCEEDINGS{eck+bertinmahieux+lamere+green:nips2007,
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fsavard
parents:
diff changeset
1851 author = {Eck, Douglas and Lamere, Paul and Bertin-Mahieux, Thierry and Green, Stephen},
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parents:
diff changeset
1852 editor = {Platt, John and Kolen, J. and Singer, Yoram and Roweis, S.},
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parents:
diff changeset
1853 title = {Automatic Generation of Social Tags for Music Recommendation},
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fsavard
parents:
diff changeset
1854 year = {2008},
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fsavard
parents:
diff changeset
1855 crossref = {NIPS20-shorter},
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fsavard
parents:
diff changeset
1856 source = "OwnPublication"
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fsavard
parents:
diff changeset
1857 }
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fsavard
parents:
diff changeset
1858
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1859 @INPROCEEDINGS{eck+bertinmahieux+lamere:ismir2007,
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1860 author = {Eck, Douglas and Bertin-Mahieux, Thierry and Lamere, Paul},
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fsavard
parents:
diff changeset
1861 title = {Autotagging music using supervised machine learning},
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fsavard
parents:
diff changeset
1862 booktitle = {{Proceedings of the 8th International Conference on Music Information Retrieval ({ISMIR} 2007)}},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1863 year = {2007},
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fsavard
parents:
diff changeset
1864 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1865 }
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fsavard
parents:
diff changeset
1866
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1867 @INPROCEEDINGS{eck+casagrande:ismir2005,
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fsavard
parents:
diff changeset
1868 author = {Eck, Douglas and Casagrande, Norman},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1869 title = {Finding Meter in Music Using an Autocorrelation Phase Matrix and Shannon Entropy},
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fsavard
parents:
diff changeset
1870 booktitle = {{Proceedings of the 6th International Conference on Music Information Retrieval ({ISMIR} 2005)}},
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fsavard
parents:
diff changeset
1871 year = {2005},
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parents:
diff changeset
1872 pages = {504--509},
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fsavard
parents:
diff changeset
1873 url = {http://www.iro.umontreal.ca/~eckdoug/papers/2005_ismir.pdf},
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fsavard
parents:
diff changeset
1874 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1875 sourcetype={Conference},
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fsavard
parents:
diff changeset
1876 }
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fsavard
parents:
diff changeset
1877
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1878 @INCOLLECTION{eck+gasser+port:2000,
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fsavard
parents:
diff changeset
1879 author = {Eck, Douglas and Gasser, M. and Port, Robert},
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parents:
diff changeset
1880 editor = {Desain, P. and Windsor, L.},
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fsavard
parents:
diff changeset
1881 title = {Dynamics and Embodiment in Beat Induction},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1882 booktitle = {{Rhythm Perception and Production}},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1883 year = {2000},
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fsavard
parents:
diff changeset
1884 pages = {157--170},
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fsavard
parents:
diff changeset
1885 publisher = {Swets and Zeitlinger},
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fsavard
parents:
diff changeset
1886 url = {http://www.iro.umontreal.ca/~eckdoug/papers/2000_rppw.pdf},
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parents:
diff changeset
1887 abstract = {We provide an argument for using dynamical systems theory in the domain of beat induction. We motivate the study of beat induction and to relate beat induction to the more general study of human rhythm cognition. In doing so we compare a dynamical, embodied approach to a symbolic (traditional AI) one, paying particular attention to how the modeling approach brings with it tacit assumptions about what is being modeled. Please note that this is a philosophy paper about research that was, at the time of writing, very much in progress.},
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parents:
diff changeset
1888 source={OwnPublication},
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fsavard
parents:
diff changeset
1889 sourcetype={Chapter},
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fsavard
parents:
diff changeset
1890 }
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fsavard
parents:
diff changeset
1891
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
1892 @INPROCEEDINGS{eck+gasser:1996,
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parents:
diff changeset
1893 author = {Eck, Douglas and Gasser, M.},
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parents:
diff changeset
1894 editor = {},
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fsavard
parents:
diff changeset
1895 title = {Perception of Simple Rhythmic Patterns in a Network of Oscillators},
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fsavard
parents:
diff changeset
1896 booktitle = {{The Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society}},
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fsavard
parents:
diff changeset
1897 year = {1996},
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parents:
diff changeset
1898 publisher = {Lawrence Erlbaum Associates},
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fsavard
parents:
diff changeset
1899 abstract = {This paper is concerned with the complex capacity to recognize and reproduce rhythmic patterns. While this capacity has not been well investigated, in broad qualitative terms it is clear that people can learn to identify and produce recurring patterns defined in terms of sequences of beats of varying intensity and rests: the rhythms behind waltzes, reels, sambas, etc. Our short term goal is a model which is "hard-wired" with knowledge of a set of such patterns. Presented with a portion of one of the patterns or a label for a pattern, the model should reproduce the pattern and continue to do so when the input is turned off. Our long-term goal is a model which can learn to adjust the connection strengths which implement particular patterns as it is exposed to input patterns.},
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parents:
diff changeset
1900 source={OwnPublication},
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parents:
diff changeset
1901 sourcetype={Conference},
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fsavard
parents:
diff changeset
1902 }
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fsavard
parents:
diff changeset
1903
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
1904 @TECHREPORT{eck+graves+schmidhuber:tr-speech2003,
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fsavard
parents:
diff changeset
1905 author = {Eck, Douglas and Graves, A. and Schmidhuber, Juergen},
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parents:
diff changeset
1906 title = {A New Approach to Continuous Speech Recognition Using {LSTM} Recurrent Neural Networks},
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fsavard
parents:
diff changeset
1907 number = {IDSIA-14-03},
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parents:
diff changeset
1908 year = {2003},
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parents:
diff changeset
1909 institution = {IDSIA},
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fsavard
parents:
diff changeset
1910 abstract = {This paper presents an algorithm for continuous speech recognition built from two Long Short-Term Memory ({LSTM}) recurrent neural networks. A first {LSTM} network performs frame-level phone probability estimation. A second network maps these phone predictions onto words. In contrast to {HMM}s, this allows greater exploitation of long-timescale correlations. Simulation results are presented for a hand-segmented subset of the "Numbers-95" database. These results include isolated phone prediction, continuous frame-level phone prediction and continuous word prediction. We conclude that despite its early stage of development, our new model is already competitive with existing approaches on certain aspects of speech recognition and promising on others, warranting further research.},
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parents:
diff changeset
1911 source={OwnPublication},
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parents:
diff changeset
1912 sourcetype={TechReport},
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parents:
diff changeset
1913 }
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parents:
diff changeset
1914
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parents:
diff changeset
1915 @TECHREPORT{eck+lapalme:2008,
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parents:
diff changeset
1916 author = {Eck, Douglas and Lapalme, J.},
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parents:
diff changeset
1917 title = {Learning Musical Structure Directly from Sequences of Music},
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parents:
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1918 number = {1300},
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parents:
diff changeset
1919 year = {2008},
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parents:
diff changeset
1920 institution = {Universit{\'{e}} de Montr{\'{e}}al DIRO},
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parents:
diff changeset
1921 url = {http://www.iro.umontreal.ca/~eckdoug/papers/tr1300.pdf},
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parents:
diff changeset
1922 source={OwnPublication},
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parents:
diff changeset
1923 sourcetype={TechReport},
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parents:
diff changeset
1924 }
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diff changeset
1925
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diff changeset
1926 @INPROCEEDINGS{eck+schmidhuber:icann2002,
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fsavard
parents:
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1927 author = {Eck, Douglas and Schmidhuber, Juergen},
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1928 editor = {Dorronsoro, J.},
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parents:
diff changeset
1929 title = {Learning The Long-Term Structure of the Blues},
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1930 booktitle = {{Artificial Neural Networks -- ICANN 2002 (Proceedings)}},
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parents:
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1931 volume = {},
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1932 year = {2002},
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1933 pages = {284--289},
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1934 publisher = {Springer},
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1935 url = {http://www.iro.umontreal.ca/~eckdoug/papers/2002_icannMusic.pdf},
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1936 abstract = {In general music composed by recurrent neural networks ({RNN}s) suffers from a lack of global structure. Though networks can learn note-by-note transition probabilities and even reproduce phrases, they have been unable to learn an entire musical form and use that knowledge to guide composition. In this study, we describe model details and present experimental results showing that {LSTM} successfully learns a form of blues music and is able to compose novel (and some listeners believe pleasing) melodies in that style. Remarkably, once the network has found the relevant structure it does not drift from it: {LSTM} is able to play the blues with good timing and proper structure as long as one is willing to listen.},
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1937 source={OwnPublication},
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parents:
diff changeset
1938 sourcetype={Conference},
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parents:
diff changeset
1939 }
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1940
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diff changeset
1941 @INPROCEEDINGS{eck+schmidhuber:ieee2002,
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parents:
diff changeset
1942 author = {Eck, Douglas and Schmidhuber, Juergen},
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parents:
diff changeset
1943 editor = {Bourlard, H.},
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parents:
diff changeset
1944 title = {Finding Temporal Structure in Music: Blues Improvisation with {LSTM} Recurrent Networks},
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parents:
diff changeset
1945 booktitle = {Neural Networks for Signal Processing XII, Proceedings of the 2002 IEEE Workshop},
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parents:
diff changeset
1946 year = {2002},
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parents:
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1947 pages = {747--756},
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parents:
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1948 publisher = {IEEE},
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1949 url = {http://www.iro.umontreal.ca/~eckdoug/papers/2002_ieee.pdf},
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1950 abstract = {Few types of signal streams are as ubiquitous as music. Here we consider the problem of extracting essential ingredients of music signals, such as well-defined global temporal structure in the form of nested periodicities (or {\em meter}). Can we construct an adaptive signal processing device that learns by example how to generate new instances of a given musical style? Because recurrent neural networks can in principle learn the temporal structure of a signal, they are good candidates for such a task. Unfortunately, music composed by standard recurrent neural networks ({RNN}s) often lacks global coherence. The reason for this failure seems to be that {RNN}s cannot keep track of temporally distant events that indicate global music structure. Long Short-Term Memory ({LSTM}) has succeeded in similar domains where other {RNN}s have failed, such as timing \& counting and learning of context sensitive languages. In the current study we show that {LSTM} is also a good mechanism for learning to compose music. We present experimental results showing that {LSTM} successfully learns a form of blues music and is able to compose novel (and we believe pleasing) melodies in that style. Remarkably, once the network has found the relevant structure it does not drift from it: {LSTM} is able to play the blues with good timing and proper structure as long as one is willing to listen.},
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1951 source={OwnPublication},
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parents:
diff changeset
1952 sourcetype={Conference},
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parents:
diff changeset
1953 }
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diff changeset
1954
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parents:
diff changeset
1955 @ARTICLE{eck+scott:2005,
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parents:
diff changeset
1956 author = {Eck, Douglas and Scott, S. K.},
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parents:
diff changeset
1957 title = {Editorial: New Research in Rhythm Perception and Production},
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parents:
diff changeset
1958 journal = {Music Perception},
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parents:
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1959 volume = {22},
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parents:
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1960 number = {3},
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parents:
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1961 year = {2005},
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parents:
diff changeset
1962 pages = {371-388},
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parents:
diff changeset
1963 source={OwnPublication},
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parents:
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1964 sourcetype={Other},
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parents:
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1965 }
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1966
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diff changeset
1967 @MISC{eck+scott:editor2005,
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diff changeset
1968 author = {Eck, Douglas and Scott, S. K.},
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parents:
diff changeset
1969 title = {Music Perception},
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parents:
diff changeset
1970 year = {2005},
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parents:
diff changeset
1971 note = {Guest Editor, Special Issue on Rhythm Perception and Production, 22(3)},
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parents:
diff changeset
1972 source={OwnPublication},
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parents:
diff changeset
1973 sourcetype={Other},
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parents:
diff changeset
1974 }
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parents:
diff changeset
1975
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diff changeset
1976 @INPROCEEDINGS{eck:1999,
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parents:
diff changeset
1977 author = {Eck, Douglas},
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parents:
diff changeset
1978 editor = {},
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parents:
diff changeset
1979 title = {Learning Simple Metrical Preferences in a Network of {F}itzhugh-{N}agumo Oscillators},
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parents:
diff changeset
1980 booktitle = {{The Proceedings of the Twenty-First Annual Conference of the Cognitive Science Society}},
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parents:
diff changeset
1981 year = {1999},
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parents:
diff changeset
1982 publisher = {Lawrence Erlbaum Associates},
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parents:
diff changeset
1983 abstract = {Hebbian learning is used to train a network of oscillators to prefer periodic signals of pulses over aperiodic signals. Target signals consisted of metronome-like voltage pulses with varying amounts of inter-onset noise injected. (with 0\% noise yielding a periodic signal and more noise yielding more and more aperiodic signals.) The oscillators---piecewise-linear approximations (Abbott, 1990) to Fitzhugh-Nagumo oscillators---are trained using mean phase coherence as an objective function. Before training a network is shown to readily synchronize with signals having wide range of noise. After training on a series of noise-free signals, a network is shown to only synchronize with signals having little or no noise. This represents a bias towards periodicity and is explained by strong positive coupling connections between oscillators having harmonically-related periods.},
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parents:
diff changeset
1984 source={OwnPublication},
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parents:
diff changeset
1985 sourcetype={Conference},
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parents:
diff changeset
1986 }
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parents:
diff changeset
1987
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parents:
diff changeset
1988 @UNPUBLISHED{eck:bramsworkshop2004,
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parents:
diff changeset
1989 author = {Eck, Douglas},
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parents:
diff changeset
1990 title = {Challenges for Machine Learning in the Domain of Music},
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parents:
diff changeset
1991 year = {2004},
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parents:
diff changeset
1992 note = {BRAMS Workshop on Brain and Music, Montreal Neurological Institute},
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parents:
diff changeset
1993 abstract = {Slides and musical examples available on request.},
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parents:
diff changeset
1994 source={OwnPublication},
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parents:
diff changeset
1995 sourcetype={Workshop},
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parents:
diff changeset
1996 optkey={""},
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parents:
diff changeset
1997 optmonth={""},
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parents:
diff changeset
1998 optannote={""},
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parents:
diff changeset
1999 }
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parents:
diff changeset
2000
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parents:
diff changeset
2001 @PHDTHESIS{eck:diss,
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parents:
diff changeset
2002 author = {Eck, Douglas},
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
2003 title = {{Meter Through Synchrony: Processing Rhythmical Patterns with Relaxation Oscillators}},
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parents:
diff changeset
2004 year = {2000},
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parents:
diff changeset
2005 school = {Indiana University, Bloomington, IN, www.idsia.ch/\-\~{}doug/\-publications.html},
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parents:
diff changeset
2006 abstract = {This dissertation uses a network of relaxation oscillators to beat along with temporal signals. Relaxation oscillators exhibit interspersed slow-fast movement and model a wide array of biological oscillations. The model is built up gradually: first a single relaxation oscillator is exposed to rhythms and shown to be good at finding downbeats in them. Then large networks of oscillators are mutually coupled in an exploration of their internal synchronization behavior. It is demonstrated that appropriate weights on coupling connections cause a network to form multiple pools of oscillators having stable phase relationships. This is a promising first step towards networks that can recreate a rhythmical pattern from memory. In the full model, a coupled network of relaxation oscillators is exposed to rhythmical patterns. It is shown that the network finds downbeats in patterns while continuing to exhibit good internal stability. A novel non-dynamical model of downbeat induction called the Normalized Positive (NP) clock model is proposed, analyzed, and used to generate comparison predictions for the oscillator model. The oscillator model compares favorably to other dynamical approaches to beat induction such as adaptive oscillators. However, the relaxation oscillator model takes advantage of intrinsic synchronization stability to allow the creation of large coupled networks. This research lays the groundwork for a long-term research goal, a robotic arm that responds to rhythmical signals by tapping along. It also opens the door to future work in connectionist learning of long rhythmical patterns.},
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parents:
diff changeset
2007 source={OwnPublication},
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parents:
diff changeset
2008 sourcetype={Thesis},
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parents:
diff changeset
2009 }
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fsavard
parents:
diff changeset
2010
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parents:
diff changeset
2011 @INPROCEEDINGS{eck:icann2001,
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parents:
diff changeset
2012 author = {Eck, Douglas},
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parents:
diff changeset
2013 editor = {Dorffner, Georg},
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fsavard
parents:
diff changeset
2014 title = {A Network of Relaxation Oscillators that Finds Downbeats in Rhythms},
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fsavard
parents:
diff changeset
2015 booktitle = {{Artificial Neural Networks -- ICANN 2001 (Proceedings)}},
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fsavard
parents:
diff changeset
2016 volume = {},
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fsavard
parents:
diff changeset
2017 year = {2001},
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parents:
diff changeset
2018 pages = {1239--1247},
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fsavard
parents:
diff changeset
2019 publisher = {Springer},
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parents:
diff changeset
2020 url = {http://www.iro.umontreal.ca/~eckdoug/papers/2001_icann.pdf},
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parents:
diff changeset
2021 abstract = {A network of relaxation oscillators is used to find downbeats in rhythmical patterns. In this study, a novel model is described in detail. Its behavior is tested by exposing it to patterns having various levels of rhythmic complexity. We analyze the performance of the model and relate its success to previous work dealing with fast synchrony in coupled oscillators.},
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parents:
diff changeset
2022 source={OwnPublication},
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fsavard
parents:
diff changeset
2023 sourcetype={Conference},
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fsavard
parents:
diff changeset
2024 }
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fsavard
parents:
diff changeset
2025
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2026 @INPROCEEDINGS{eck:icassp2007,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2027 author = {Eck, Douglas},
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parents:
diff changeset
2028 editor = {},
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fsavard
parents:
diff changeset
2029 title = {Beat Tracking Using an Autocorrelation Phase Matrix},
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fsavard
parents:
diff changeset
2030 booktitle = {{Proceedings of the 2007 International Conference on Acoustics, Speech and Signal Processing (ICASSP)}},
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parents:
diff changeset
2031 year = {2007},
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parents:
diff changeset
2032 pages = {1313--1316},
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parents:
diff changeset
2033 publisher = {IEEE Signal Processing Society},
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parents:
diff changeset
2034 url = {http://www.iro.umontreal.ca/~eckdoug/papers/2007_icassp.pdf},
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parents:
diff changeset
2035 source={OwnPublication},
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parents:
diff changeset
2036 sourcetype={Conference},
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parents:
diff changeset
2037 }
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parents:
diff changeset
2038
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
2039 @INPROCEEDINGS{eck:icmpc2004,
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parents:
diff changeset
2040 author = {Eck, Douglas},
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parents:
diff changeset
2041 editor = {Lipscomb, S. D. and Ashley, R. and Gjerdingen, R. O. and Webster, P.},
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parents:
diff changeset
2042 title = {A Machine-Learning Approach to Musical Sequence Induction That Uses Autocorrelation to Bridge Long Timelags},
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parents:
diff changeset
2043 booktitle = {{The Proceedings of the Eighth International Conference on Music Perception and Cognition ({ICMPC}8)}},
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parents:
diff changeset
2044 year = {2004},
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parents:
diff changeset
2045 pages = {542-543},
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parents:
diff changeset
2046 publisher = {Causal Productions},
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parents:
diff changeset
2047 abstract = {One major challenge in using statistical sequence learning methods in the domain of music lies in bridging the long timelags that separate important musical events. Consider, for example, the chord changes that convey the basic structure of a pop song. A sequence learner that cannot predict chord changes will almost certainly not be able to generate new examples in a musical style or to categorize songs by style. Yet, it is surprisingly difficult for a sequence learner to bridge the long timelags necessary to identify when a chord change will occur and what its new value will be. This is the case because chord changes can be separated by dozens or hundreds of intervening notes. One could solve this problem by treating chords as being special (as did Mozer, NIPS 1991). But this is impractical---it requires chords to be labeled specially in the dataset, limiting the applicability of the model to non-labeled examples---and furthermore does not address the general issue of nested temporal structure in music. I will briefly describe this temporal structure (known commonly as "meter") and present a model that uses to its advantage an assumption that sequences are metrical. The model consists of an autocorrelation-based filtration that estimates online the most likely metrical tree (i.e. the frequency and phase of beat, measure, phrase &etc.) and uses that to generate a series of sequences varying at different rates. These sequences correspond to each level in the hierarchy. Multiple learners can be used to treat each series separately and their predictions can be combined to perform composition and categorization. I will present preliminary results that demonstrate the usefulness of this approach. Time permitting I will also compare the model to alternate approaches.},
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parents:
diff changeset
2048 source={OwnPublication},
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parents:
diff changeset
2049 sourcetype={Conference},
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parents:
diff changeset
2050 }
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parents:
diff changeset
2051
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parents:
diff changeset
2052 @INPROCEEDINGS{eck:icmpc2006,
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parents:
diff changeset
2053 author = {Eck, Douglas},
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parents:
diff changeset
2054 editor = {Baroni, M. and Addessi, A. R. and Caterina, R. and Costa, M.},
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parents:
diff changeset
2055 title = {Beat Induction Using an Autocorrelation Phase Matrix},
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parents:
diff changeset
2056 booktitle = {The Proceedings of the 9th International Conference on Music Perception and Cognition ({ICMPC9})},
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parents:
diff changeset
2057 year = {2006},
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parents:
diff changeset
2058 pages = {931-932},
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parents:
diff changeset
2059 publisher = {Causal Productions},
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parents:
diff changeset
2060 source={OwnPublication},
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parents:
diff changeset
2061 sourcetype={Conference},
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parents:
diff changeset
2062 }
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parents:
diff changeset
2063
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parents:
diff changeset
2064 @UNPUBLISHED{eck:irisworkshop2004,
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fsavard
parents:
diff changeset
2065 author = {Eck, Douglas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2066 title = {Using Autocorrelation to Bridge Long Timelags when Learning Sequences of Music},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2067 year = {2004},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2068 note = {IRIS 2004 Machine Learning Workshop, Ottawa, Canada},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2069 abstract = {Slides and musical examples available on request.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2070 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2071 sourcetype={Workshop},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2072 optkey={""},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2073 optmonth={""},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2074 optannote={""},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2075 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2076
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2077 @ARTICLE{eck:jnmr2001,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2078 author = {Eck, Douglas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2079 title = {A Positive-Evidence Model for Rhythmical Beat Induction},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2080 journal = {Journal of New Music Research},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2081 volume = {30},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2082 number = {2},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2083 year = {2001},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2084 pages = {187--200},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2085 abstract = {The Normalized Positive (NPOS) model is a rule-based model that predicts downbeat location and pattern complexity in rhythmical patterns. Though derived from several existing models, the NPOS model is particularly effective at making correct predictions while at the same time having low complexity. In this paper, the details of the model are explored and a comparison is made to existing models. Several datasets are used to examine the complexity predictions of the model. Special attention is paid to the model's ability to account for the effects of musical experience on beat induction.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2086 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2087 sourcetype={Journal},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2088 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2089
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2090 @UNPUBLISHED{eck:mipsworkshop2004,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2091 author = {Eck, Douglas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2092 title = {Bridging Long Timelags in Music},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2093 year = {2004},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2094 note = {NIPS 2004 Workshop on Music and Machine Learning (MIPS), Whistler, British Columbia},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2095 abstract = {Slides and musical examples available on request.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2096 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2097 sourcetype={Workshop},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2098 optkey={""},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2099 optmonth={""},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2100 optannote={""},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2101 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2102
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2103 @ARTICLE{eck:mp2006,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2104 author = {Eck, Douglas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2105 title = {Finding Long-Timescale Musical Structure with an Autocorrelation Phase Matrix},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2106 journal = {Music Perception},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2107 volume = {24},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2108 number = {2},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2109 year = {2006},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2110 pages = {167--176},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2111 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2112 sourcetype={Journal},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2113 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2114
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2115 @UNPUBLISHED{eck:nipsworkshop2003,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2116 author = {Eck, Douglas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2117 title = {Time-warped hierarchical structure in music and speech: A sequence prediction challenge},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2118 year = {2003},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2119 note = {NIPS 2003 Workshop on Recurrent Neural Networks, Whistler, British Columbia},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2120 abstract = {Slides and musical examples available on request.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2121 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2122 sourcetype={Workshop},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2123 optkey={""},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2124 optmonth={""},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2125 optannote={""},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2126 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2127
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2128 @UNPUBLISHED{eck:nipsworkshop2006,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2129 author = {Eck, Douglas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2130 title = {Generating music sequences with an echo state network},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2131 year = {2006},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2132 note = {NIPS 2006 Workshop on Echo State Networks and Liquid State Machines},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2133 abstract = {Slides and musical examples available on request.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2134 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2135 sourcetype={Workshop},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2136 optkey={""},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2137 optmonth={""},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2138 optannote={""},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2139 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2140
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2141 @UNPUBLISHED{eck:nipsworkshop2007,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2142 author = {Eck, Douglas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2143 title = {Measuring and modeling musical expression},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2144 year = {2007},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2145 note = {NIPS 2007 Workshop on Music, Brain and Cognition},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2146 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2147 sourcetype={Workshop},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2148 optkey={""},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2149 optmonth={""},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2150 optannote={""},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2151 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2152
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2153 @ARTICLE{eck:psyres2002,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2154 author = {Eck, Douglas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2155 title = {Finding Downbeats with a Relaxation Oscillator},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2156 journal = {Psychol. Research},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2157 volume = {66},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2158 number = {1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2159 year = {2002},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2160 pages = {18--25},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2161 abstract = {A relaxation oscillator model of neural spiking dynamics is applied to the task of finding downbeats in rhythmical patterns. The importance of downbeat discovery or {\em beat induction} is discussed, and the relaxation oscillator model is compared to other oscillator models. In a set of computer simulations the model is tested on 35 rhythmical patterns from Povel \& Essens (1985). The model performs well, making good predictions in 34 of 35 cases. In an analysis we identify some shortcomings of the model and relate model behavior to dynamical properties of relaxation oscillators.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2162 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2163 sourcetype={Journal},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2164 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2165
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2166 @UNPUBLISHED{eck:rppw2005,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2167 author = {Eck, Douglas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2168 title = {Meter and Autocorrelation},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2169 year = {2005},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2170 note = {{10th Rhythm Perception and Production Workshop (RPPW), Alden Biesen, Belgium}},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2171 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2172 sourcetype={Workshop},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2173 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2174
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2175 @TECHREPORT{eck:tr-music2002,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2176 author = {Eck, Douglas and Schmidhuber, Juergen},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2177 title = {A First Look at Music Composition using {LSTM} Recurrent Neural Networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2178 number = {IDSIA-07-02},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2179 year = {2002},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2180 institution = {IDSIA},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2181 abstract = {In general music composed by recurrent neural networks ({RNN}s) suffers from a lack of global structure. Though networks can learn note-by-note transition probabilities and even reproduce phrases, attempts at learning an entire musical form and using that knowledge to guide composition have been unsuccessful. The reason for this failure seems to be that {RNN}s cannot keep track of temporally distant events that indicate global music structure. Long Short-Term Memory ({LSTM}) has succeeded in similar domains where other {RNN}s have failed, such as timing \& counting and CSL learning. In the current study I show that {LSTM} is also a good mechanism for learning to compose music. I compare this approach to previous attempts, with particular focus on issues of data representation. I present experimental results showing that {LSTM} successfully learns a form of blues music and is able to compose novel (and I believe pleasing) melodies in that style. Remarkably, once the network has found the relevant structure it does not drift from it: {LSTM} is able to play the blues with good timing and proper structure as long as one is willing to listen. {\em Note: This is a more complete version of the 2002 ICANN submission Learning the Long-Term Structure of the Blues.}},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2182 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2183 sourcetype={TechReport},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2184 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2185
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2186 @TECHREPORT{eck:tr-npos2000,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2187 author = {Eck, Douglas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2188 title = {A Positive-Evidence Model for Classifying Rhythmical Patterns},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2189 number = {IDSIA-09-00},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2190 year = {2000},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2191 institution = {IDSIA},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2192 abstract = {The Normalized Positive (NPOS) model is a novel matching model that predicts downbeat location and pattern complexity in rhythmical patterns. Though similar models report success, the NPOS model is particularly effective at making these predictions while at the same time being theoretically and mathematically simple. In this paper, the details of the model are explored and a comparison is made to existing models. Several datasets are used to examine the complexity predictions of the model. Special attention is paid to the model's ability to account for the effects of musical experience on rhythm perception.\\ {\em Note: See the 2001 Journal of New Music Research paper "A Positive-Evidence Model for Rhythmical Beat Induction" for a newer version of this paper.}},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2193 ps={ftp://ftp.idsia.ch/pub/techrep/IDSIA-09-00.ps.gz},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2194 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2195 sourcetype={TechReport},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2196 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2197
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2198 @TECHREPORT{eck:tr-oscnet2001,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2199 author = {Eck, Douglas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2200 title = {A Network of Relaxation Oscillators that Finds Downbeats in Rhythms},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2201 number = {IDSIA-06-01},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2202 year = {2001},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2203 institution = {IDSIA},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2204 abstract = {A network of relaxation oscillators is used to find downbeats in rhythmical patterns. In this study, a novel model is described in detail. Its behavior is tested by exposing it to patterns having various levels of rhythmic complexity. We analyze the performance of the model and relate its success to previous work dealing with fast synchrony in coupled oscillators. \\ {\em Note: See the 2001 ICANN conference proceeding by the same title for a newer version of this paper.}},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2205 ps={ftp://ftp.idsia.ch/pub/techrep/IDSIA-06-01.ps.gz},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2206 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2207 sourcetype={TechReport},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2208 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2209
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2210 @TECHREPORT{eck:tr-tracking2000,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2211 author = {Eck, Douglas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2212 title = {Tracking Rhythms with a Relaxation Oscillator},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2213 number = {IDSIA-10-00},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2214 year = {2000},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2215 institution = {IDSIA},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2216 abstract = {A number of biological and mechanical processes are typified by a continued slow accrual and fast release of energy. A nonlinear oscillator exhibiting this slow-fast behavior is called a relaxation oscillator and is used to model, for example, human heartbeat pacemaking and neural action potential. Similar limit cycle oscillators are used to model a wider range of behaviors including predator-prey relationships and synchrony in animal populations such as fireflies. Though nonlinear limit-cycle oscillators have been successfully applied to beat induction, relaxation oscillators have received less attention. In this work we offer a novel and effective relaxation oscillator model of beat induction. We outline the model in detail and provide a perturbation analysis of its response to external stimuli. In a series of simulations we expose the model to patterns from Experiment 1 of Povel \& Essens (1985). We then examine the beat assignments of the model. Although the overall performance of the model is very good, there are shortcomings. We believe that a network of mutually-coupled oscillators will address many of these shortcomings, and we suggest an appropriate course for future research.\\ {\em Note: See the 2001 {\em Psychological Research} article "Finding Downbeats with a Relaxation Oscillator" for a revised but less detailed version of this paper.}},
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parents:
diff changeset
2217 ps={ftp://ftp.idsia.ch/pub/techrep/IDSIA-10-00.ps.gz},
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parents:
diff changeset
2218 source={OwnPublication},
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parents:
diff changeset
2219 sourcetype={TechReport},
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parents:
diff changeset
2220 }
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parents:
diff changeset
2221
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parents:
diff changeset
2222 @TECHREPORT{eck:tr-tracking2002,
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parents:
diff changeset
2223 author = {Eck, Douglas},
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parents:
diff changeset
2224 title = {Real-Time Musical Beat Induction with Spiking Neural Networks},
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parents:
diff changeset
2225 number = {IDSIA-22-02},
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parents:
diff changeset
2226 year = {2002},
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parents:
diff changeset
2227 institution = {IDSIA},
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parents:
diff changeset
2228 abstract = {Beat induction is best described by analogy to the activities of hand clapping or foot tapping, and involves finding important metrical components in an auditory signal, usually music. Though beat induction is intuitively easy to understand it is difficult to define and still more difficult to perform automatically. We will present a model of beat induction that uses a spiking neural network as the underlying synchronization mechanism. This approach has some advantages over existing methods; it runs online, responds at many levels in the metrical hierarchy, and produces good results on performed music (Beatles piano performances encoded as MIDI). In this paper the model is described in some detail and simulation results are discussed.},
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parents:
diff changeset
2229 source={OwnPublication},
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parents:
diff changeset
2230 sourcetype={TechReport},
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parents:
diff changeset
2231 }
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parents:
diff changeset
2232
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parents:
diff changeset
2233 @UNPUBLISHED{eck:verita2002,
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parents:
diff changeset
2234 author = {Eck, Douglas},
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parents:
diff changeset
2235 title = {Real Time Beat Induction with Spiking Neurons},
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parents:
diff changeset
2236 year = {2002},
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parents:
diff changeset
2237 note = {{Music, Motor Control and the Mind: Symposium at Monte Verita, May}},
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parents:
diff changeset
2238 abstract = {Beat induction is best described by analogy to the activites of hand clapping or foot tapping, and involves finding important metrical components in an auditory signal, usually music. Though beat induction is intuitively easy to understand it is difficult to define and still more difficult to model. I will discuss an approach to beat induction that uses a network of spiking neurons to synchronize with periodic components in a signal at many timescales. Through a competitive process, groups of oscillators embodying a particular metrical interpretation (e.g. \"4/4\") are selected from the network and used to track the pattern. I will compare this model to other approaches including a traditional symbolic AI system (Dixon 2001), and one based on Bayesian statistics (Cemgil et al, 2001). Finally I will present performance results of the network on a set of MIDI-recorded piano performances of Beatles songs collected by the Music, Mind, Machine Group, NICI, University of Nijmegen (see Cemgil et al, 2001 for more details or http://www.nici.kun.nl/mmm).},
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parents:
diff changeset
2239 source={OwnPublication},
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parents:
diff changeset
2240 sourcetype={Workshop},
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parents:
diff changeset
2241 }
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parents:
diff changeset
2242
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parents:
diff changeset
2243 @INPROCEEDINGS{ElHihi+Bengio-nips8,
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parents:
diff changeset
2244 author = {El Hihi, Salah and Bengio, Yoshua},
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parents:
diff changeset
2245 title = {Hierarchical Recurrent Neural Networks for Long-Term Dependencies},
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parents:
diff changeset
2246 year = {1996},
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parents:
diff changeset
2247 crossref = {NIPS8-shorter},
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parents:
diff changeset
2248 abstract = {We have already shown that extracting lone-term dependencies from sequential data is difficult, both for deterministic dynamical systems such as recurrent networks, and probabilistic models such as hidden {Markov} models ({HMM}s) or input/output hidden {Markov} models ({IOHMM}s). In practice, to avoid this problem, researchers have used domain specific a-priori knowledge to give meaning to the hidden or state variables representing past context. In this paper we propose to use a more general type of a-priori knowledge, namely that the temporal dependencies are structured hierarchically. This implies that long-term dependencies are represented by variables with a long time scale. This principle is applied to a recurrent network which includes delays and multiple time scales. Experiments confirm the advantages of such structures. A similar approach is proposed for {HMM}s and {IOHMM}s.},
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parents:
diff changeset
2249 topics={LongTerm},cat={C},
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parents:
diff changeset
2250 }
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parents:
diff changeset
2251
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parents:
diff changeset
2252 @ARTICLE{Erhan+al-2010,
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parents:
diff changeset
2253 author = {Erhan, Dumitru and Bengio, Yoshua and Courville, Aaron and Manzagol, Pierre-Antoine and Vincent, Pascal and Bengio, Samy},
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parents:
diff changeset
2254 title = {Why Does Unsupervised Pre-training Help Deep Learning?},
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parents:
diff changeset
2255 volume = {11},
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parents:
diff changeset
2256 year = {2010},
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parents:
diff changeset
2257 pages = {625--660},
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parents:
diff changeset
2258 journal = {Journal of Machine Learning Research},
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parents:
diff changeset
2259 abstract = {Much recent research has been devoted to learning algorithms for deep architectures such as Deep Belief Networks and stacks of auto-encoder variants, with impressive results obtained in several areas, mostly on vision and language datasets. The best results obtained on supervised learning tasks involve an unsupervised learning component, usually in an unsupervised pre-training phase. Even though these new algorithms have enabled training deep models, many questions remain as to the nature of this difficult learning problem. The main question investigated here is the following: why does unsupervised pre-training work and why does it work so well? Answering these questions is important if learning in deep architectures is to be further improved. We propose several explanatory hypotheses and test them through extensive simulations. We empirically show the influence of pre-training with respect to architecture depth, model capacity, and number of training examples. The experiments confirm and clarify the advantage of unsupervised pre-training. The results suggest that unsupervised pre-training guides the learning towards basins of attraction of minima that are better in terms of the underlying data distribution; the evidence from these results supports a regularization explanation for the effect of pre-training.}
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parents:
diff changeset
2260 }
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parents:
diff changeset
2261
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parents:
diff changeset
2262 @INPROCEEDINGS{Erhan-aistats-2010,
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parents:
diff changeset
2263 author = {Erhan, Dumitru and Courville, Aaron and Bengio, Yoshua and Vincent, Pascal},
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fsavard
parents:
diff changeset
2264 title = {Why Does Unsupervised Pre-training Help Deep Learning?},
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fsavard
parents:
diff changeset
2265 booktitle = {Proceedings of AISTATS 2010},
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fsavard
parents:
diff changeset
2266 volume = {9},
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fsavard
parents:
diff changeset
2267 year = {2010},
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parents:
diff changeset
2268 pages = {201-208},
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parents:
diff changeset
2269 abstract = {Much recent research has been devoted to learning algorithms for deep architectures such as Deep Belief Networks and stacks of auto-encoder variants with impressive results being obtained in several areas, mostly on vision and language datasets. The best results obtained on supervised learning tasks often involve an unsupervised learning component, usually in an unsupervised pre-training phase. The main question investigated here is the following: why does unsupervised pre-training work so well? Through extensive experimentation, we explore several possible explanations discussed in the literature including its action as a regularizer (Erhan et al. 2009) and as an aid to optimization (Bengio et al. 2007). Our results build on the work of Erhan et al. 2009, showing that unsupervised pre-training appears to play predominantly a regularization role in subsequent supervised training. However our results in an online setting, with a virtually unlimited data stream, point to a somewhat more nuanced interpretation of the roles of optimization and regularization in the unsupervised pre-training effect.}
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parents:
diff changeset
2270 }
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parents:
diff changeset
2271
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2272 @MASTERSTHESIS{Erhan-MSc,
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parents:
diff changeset
2273 author = {Erhan, Dumitru},
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fsavard
parents:
diff changeset
2274 keywords = {Apprentisage multit{\^{a}}che, Filtrage collaboratif, M{\'{e}}thodes {\`{a}} noyaux, QSAR, R{\'{e}}seaux de neurones},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2275 title = {Collaborative filtering techniques for drug discovery},
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fsavard
parents:
diff changeset
2276 year = {2006},
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parents:
diff changeset
2277 school = {Universit{\'{e}} de Montr{\'{e}}al},
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fsavard
parents:
diff changeset
2278 abstract = {Cette th{\`{e}}se examine le probl{\`{e}}me d'apprendre plusieurs t{\^{a}}ches simultan{\'{e}}ment,
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fsavard
parents:
diff changeset
2279 afin de transf{\'{e}}rer les connaissances apprises {\`{a}} une nouvelle t{\^{a}}che. Si
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fsavard
parents:
diff changeset
2280 on suppose que les t{\^{a}}ches partagent une repr{\'{e}}sentation et qu'il est possible de
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parents:
diff changeset
2281 d{\'{e}}couvrir cette repr{\'{e}}sentation efficacement, cela peut nous servir {\`{a}} construire un
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fsavard
parents:
diff changeset
2282 meilleur mod{\`{e}}le de la nouvelle t{\^{a}}che. Il existe plusieurs variantes de
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
2283 cette m{\'{e}}thode: transfert inductif, apprentisage multit{\^{a}}che, filtrage
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parents:
diff changeset
2284 collaboratif etc. Nous avons {\'{e}}valu{\'{e}} plusieurs algorithmes d'apprentisage
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parents:
diff changeset
2285 supervis{\'{e}} pour d{\'{e}}couvrir des repr{\'{e}}sentations partag{\'{e}}es parmi les
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parents:
diff changeset
2286 t{\^{a}}ches d{\'{e}}finies dans un probl{\`{e}}me de chimie computationelle. Nous avons
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parents:
diff changeset
2287 formul{\'{e}} le probl{\`{e}}me dans un cadre d'apprentisage automatique,
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fsavard
parents:
diff changeset
2288 fait l'analogie avec les algorithmes standards de filtrage collaboratif et construit les
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2289 hypoth{\`{e}}ses g{\'{e}}n{\'{e}}rales qui devraient {\^{e}}tre test{\'{e}}es pour valider l'utilitisation des
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2290 algorithmes multit{\^{a}}che. Nous avons aussi {\'{e}}valu{\'{e}} la performance des algorithmes
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fsavard
parents:
diff changeset
2291 d'apprentisage utilis{\'{e}}s et d{\'{e}}montrons qu'il est, en effet, possible de trouver une
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fsavard
parents:
diff changeset
2292 repr{\'{e}}sentation partag{\'{e}}e pour le probl{\`{e}}me consider{\'{e}}. Du point de vue
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parents:
diff changeset
2293 th{\'{e}}orique, notre apport est une modification d'un algorithme
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fsavard
parents:
diff changeset
2294 standard---les machines {\`{a}} vecteurs de support--qui produit des r{\'{e}}sultats
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fsavard
parents:
diff changeset
2295 comparables aux meilleurs algorithmes disponsibles et qui utilise {\`{a}} fond les
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fsavard
parents:
diff changeset
2296 concepts de l'apprentisage multit{\^{a}}che. Du point de vue pratique, notre
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parents:
diff changeset
2297 apport est l'utilisation de notre algorithme par les compagnies
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fsavard
parents:
diff changeset
2298 pharmaceutiques dans leur d{\'{e}}couverte de nouveaux m{\'{e}}dicaments.}
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parents:
diff changeset
2299 }
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parents:
diff changeset
2300
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parents:
diff changeset
2301 @INPROCEEDINGS{Erhan2009,
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parents:
diff changeset
2302 author = {Erhan, Dumitru and Manzagol, Pierre-Antoine and Bengio, Yoshua and Bengio, Samy and Vincent, Pascal},
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parents:
diff changeset
2303 keywords = {Deep Networks},
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fsavard
parents:
diff changeset
2304 title = {The Difficulty of Training Deep Architectures and the effect of Unsupervised Pre-Training},
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parents:
diff changeset
2305 year = {2009},
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parents:
diff changeset
2306 pages = {153--160},
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fsavard
parents:
diff changeset
2307 crossref = {xAISTATS2009-shorter},
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parents:
diff changeset
2308 abstract = {Whereas theoretical work suggests that deep architectures might be more efficient at representing highly-varying functions, training deep architectures was unsuccessful until the recent advent of algorithms based on unsupervised pretraining. Even though these new algorithms have enabled training deep models, many questions remain as to the nature of this difficult learning problem. Answering these questions is important if learning in deep architectures is to be further improved. We attempt to shed some light on these questions through extensive simulations. The experiments confirm and clarify the advantage of unsupervised pre-training. They demonstrate the robustness of the training procedure with respect to the random initialization, the positive effect of pre-training in terms of optimization and its role as a regularizer. We empirically show the influence of pre-training with respect to architecture depth, model capacity, and number of training examples.}
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parents:
diff changeset
2309 }
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parents:
diff changeset
2310
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parents:
diff changeset
2311 @ARTICLE{gasser+eck+port:1999,
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parents:
diff changeset
2312 author = {Gasser, M. and Eck, Douglas and Port, Robert},
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parents:
diff changeset
2313 title = {Meter as Mechanism: A Neural Network Model that Learns Metrical patterns},
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parents:
diff changeset
2314 journal = {Connection Science},
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parents:
diff changeset
2315 volume = {11},
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parents:
diff changeset
2316 number = {2},
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parents:
diff changeset
2317 year = {1999},
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parents:
diff changeset
2318 pages = {187--216},
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parents:
diff changeset
2319 abstract = {One kind of prosodic structure that apparently underlies both music and some examples of speech production is meter. Yet detailed measurements of the timing of both music and speech show that the nested periodicities that define metrical structure can be quite noisy in time. What kind of system could produce or perceive such variable metrical timing patterns? And what would it take to be able to store and reproduce particular metrical patterns from long-term memory? We have developed a network of coupled oscillators that both produces and perceives patterns of pulses that conform to particular meters. In addition, beginning with an initial state with no biases, it can learn to prefer the particular meter that it has been previously exposed to.},
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parents:
diff changeset
2320 own={Have},
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parents:
diff changeset
2321 source={OwnPublication},
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parents:
diff changeset
2322 sourcetype={Journal},
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parents:
diff changeset
2323 }
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parents:
diff changeset
2324
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parents:
diff changeset
2325 @TECHREPORT{gasser+eck+port:tr-1996,
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parents:
diff changeset
2326 author = {Gasser, M. and Eck, Douglas and Port, Robert},
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parents:
diff changeset
2327 title = {Meter as Mechanism A Neural Network that Learns Metrical Patterns},
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parents:
diff changeset
2328 number = {180},
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parents:
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2329 year = {1996},
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parents:
diff changeset
2330 institution = {Indiana University Cognitive Science Program},
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parents:
diff changeset
2331 source={OwnPublication},
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fsavard
parents:
diff changeset
2332 sourcetype={TechReport},
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parents:
diff changeset
2333 }
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parents:
diff changeset
2334
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parents:
diff changeset
2335 @INPROCEEDINGS{gasser+eck:1996,
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parents:
diff changeset
2336 author = {Gasser, M. and Eck, Douglas},
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parents:
diff changeset
2337 editor = {},
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parents:
diff changeset
2338 title = {Representing Rhythmic Patterns in a Network of Oscillators},
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parents:
diff changeset
2339 booktitle = {{The Proceedings of the International Conference on Music Perception and Cognition}},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2340 number = {4},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2341 year = {1996},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2342 pages = {361--366},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2343 publisher = {Lawrence Erlbaum Associates},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2344 url = {http://www.iro.umontreal.ca/~eckdoug/papers/1996_gasser_icmpc.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2345 abstract = {This paper describes an evolving computational model of the perception and pro-duction of simple rhythmic patterns. The model consists of a network of oscillators of different resting frequencies which couple with input patterns and with each other. Os-cillators whose frequencies match periodicities in the input tend to become activated. Metrical structure is represented explicitly in the network in the form of clusters of os-cillators whose frequencies and phase angles are constrained to maintain the harmonic relationships that characterize meter. Rests in rhythmic patterns are represented by ex-plicit rest oscillators in the network, which become activated when an expected beat in the pattern fails to appear. The model makes predictions about the relative difficulty of patterns and the effect of deviations from periodicity in the input.},
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parents:
diff changeset
2346 source={OwnPublication},
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fsavard
parents:
diff changeset
2347 sourcetype={Conference},
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fsavard
parents:
diff changeset
2348 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2349
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2350 @INPROCEEDINGS{gers+eck+schmidhuber:icann2001,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2351 author = {Gers, F. A. and Eck, Douglas and Schmidhuber, Juergen},
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fsavard
parents:
diff changeset
2352 editor = {Dorffner, Georg},
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fsavard
parents:
diff changeset
2353 title = {Applying {LSTM} to Time Series Predictable Through Time-Window Approaches},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2354 booktitle = {{Artificial Neural Networks -- ICANN 2001 (Proceedings)}},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2355 year = {2001},
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fsavard
parents:
diff changeset
2356 pages = {669--676},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2357 publisher = {Springer},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2358 url = {http://www.iro.umontreal.ca/~eckdoug/papers/2001_gers_icann.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2359 abstract = {Long Short-Term Memory ({LSTM}) is able to solve many time series tasks unsolvable by feed-forward networks using fixed size time windows. Here we find that {LSTM}'s superiority does {\em not} carry over to certain simpler time series tasks solvable by time window approaches: the Mackey-Glass series and the Santa Fe {FIR} laser emission series (Set A). This suggests t use {LSTM} only when simpler traditional approaches fail.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2360 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2361 sourcetype={Conference},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2362 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2363
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2364 @TECHREPORT{gers+eck+schmidhuber:tr-2000,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2365 author = {Gers, F. A. and Eck, Douglas and Schmidhuber, Juergen},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2366 title = {Applying {LSTM} to Time Series Predictable Through Time-Window Approaches},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2367 number = {IDSIA-22-00},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2368 year = {2000},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2369 institution = {IDSIA},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2370 abstract = {Long Short-Term Memory ({LSTM}) is able to solve many time series tasks unsolvable by feed-forward networks using fixed size time windows. Here we find that {LSTM}'s superiority does {\em not} carry over to certain simpler time series tasks solvable by time window approaches: the Mackey-Glass series and the Santa Fe {FIR} laser emission series (Set A). This suggests t use {LSTM} only when simpler traditional approaches fail.\\ {\em Note: See the 2001 ICANN conference proceeding by the same title for a newer version of this paper.}},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2371 ps={ftp://ftp.idsia.ch/pub/techrep/IDSIA-22-00.ps.gz},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2372 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2373 sourcetype={TechReport},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2374 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2375
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2376 @INPROCEEDINGS{gers+perez+eck+schmidhuber:esann2002,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2377 author = {Gers, F. A. and Perez-Ortiz, J. A. and Eck, Douglas and Schmidhuber, Juergen},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2378 title = {{DEKF-LSTM}},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2379 booktitle = {Proceedings of the 10th European Symposium on Artificial Neural Networks, ESANN 2002},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2380 year = {2002},
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fsavard
parents:
diff changeset
2381 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2382 sourcetype={Conference},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2383 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2384
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2385 @INPROCEEDINGS{gers+perez+eck+schmidhuber:icannA2002,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2386 author = {Gers, F. A. and Perez-Ortiz, J. A. and Eck, Douglas and Schmidhuber, Juergen},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2387 editor = {Dorronsoro, J.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2388 title = {Learning Context Sensitive Languages with {LSTM} Trained with {Kalman} Filters},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2389 booktitle = {{Artificial Neural Networks -- ICANN 2002 (Proceedings)}},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2390 year = {2002},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2391 pages = {655--660},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2392 publisher = {Springer},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2393 abstract = {Unlike traditional recurrent neural networks, the Long Short-Term Memory ({LSTM}) model generalizes well when presented with training sequences derived from regular and also simple nonregular languages. Our novel combination of {LSTM} and the decoupled extended Kalman filter, however, learns even faster and generalizes even better, requiring only the 10 shortest exemplars n <= 10 of the context sensitive language a^nb^nc^n to deal correctly with values of n up to 1000 and more. Even when we consider the relatively high update complexity per timestep, in many cases the hybrid offers faster learning than {LSTM} by itself.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2394 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2395 sourcetype={Conference},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2396 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2397
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2398 @PHDTHESIS{Ghosn-Phd-2003,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2399 author = {Ghosn, Joumana},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2400 title = {Apprentissage multi-t{\^{a}}ches et partage de connaissances},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2401 year = {2003},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2402 school = {Universit{\'{e}} de Montr{\'{e}}al}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2403 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2404
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2405 @INPROCEEDINGS{ghosn97,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2406 author = {Ghosn, Joumana and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2407 title = {Multi-Task Learning for Stock Selection},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2408 year = {1997},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2409 pages = {946--952},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2410 publisher = {MIT Press, Cambridge, MA},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2411 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/multitask-nips97.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2412 crossref = {NIPS9},
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fsavard
parents:
diff changeset
2413 abstract = {Artificial Neural Networks can be used to predict future returns of stocks in order to take financial decisions. Should one build a separate network for each stock or share the same network for all the stocks. In this paper we also explore other alternatives, in which some layers are shared and others are not shared. When the prediction of future returns for different stocks are viewed as different tasks, sharing some parameters across stocks is a form of multi-task learning. In a series of experiments with Canadian stocks, we obtain yearly returns that are more than 14\% above various benchmarks.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2414 topics={MultiTask,Finance},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2415 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2416
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2417 @TECHREPORT{Gingras-asynchronous-TR96,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2418 author = {Gingras, Fran{\c c}ois and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2419 title = {Handling asynchronous or missing financial data with recurrent networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2420 number = {1020},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2421 year = {1996},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2422 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2423 topics={Finance,Missing},cat={T},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2424 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2425
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2426 @TECHREPORT{Gingras-financial-TR99,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2427 author = {Gingras, Fran{\c c}ois and Bengio, Yoshua and Nadeau, Claude},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2428 title = {On Out-of-Sample Statistics for Financial Time-Series},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2429 number = {2585},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2430 year = {1999},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2431 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2432 topics={Comparative,Finance},cat={T},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2433 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2434
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2435 @INPROCEEDINGS{gingras2000,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2436 author = {Gingras, Fran{\c c}ois and Bengio, Yoshua and Nadeau, Claude},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2437 title = {On Out-of-Sample Statistics for Time-Series},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2438 booktitle = {Computational Finance 2000},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2439 year = {2000},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2440 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/out-err-cf2000.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2441 abstract = {This paper studies an out-of-sample statistic for time-series prediction that is analogous to the widely used R2 in-sample statistic. We propose and study methods to estimate the variance of this out-of-sample statistic. We suggest that the out-of-sample statistic is more robust to distributional and asymptotic assumptions behind many tests for in-sample statistics. Furthermore we argue that it may be more important in some cases to choose a model that generalizes as well as possible rather than choose the parameters that are closest to the true parameters. Comparative experiments are performed on a financial time-series (daily and monthly returns of the TSE300 index). The experiments are performed or varying prediction horizons and we study the relation between predictibility (out-of-sample R2), variability of the out-of-sample R2 statistic, and the prediction horizon.},
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
2442 topics={Comparative,Finance},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2443 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2444
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2445 @INPROCEEDINGS{GlorotAISTATS2010,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2446 author = {Bengio, Yoshua and Glorot, Xavier},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2447 title = {Understanding the difficulty of training deep feedforward neural networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2448 booktitle = {Proceedings of AISTATS 2010},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2449 volume = {9},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2450 year = {2010},
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fsavard
parents:
diff changeset
2451 pages = {249-256},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2452 abstract = {Whereas before 2006 it appears that deep multi-layer neural networks were not successfully trained, since then several algorithms have been shown to successfully train them, with experimental results showing the superiority of deeper vs less deep architectures. All these experimental results were obtained with new initialization or training mechanisms. Our objective here is to understand better why standard gradient descent from random initialization is doing so poorly with deep neural networks, to better understand these recent relative successes and help design better algorithms in the future. We first observe the influence of the non-linear activations functions. We find that the logistic sigmoid activation is unsuited for deep networks with random initialization because of its mean value, which can drive especially the top hidden layer into saturation. Surprisingly, we find that saturated units can move out of saturation by themselves, albeit slowly, and explaining the plateaus sometimes seen when training neural networks. We find that a new non-linearity that saturates less can often be beneficial. Finally, we study how activations and gradients vary across layers and during training, with the idea that training may be more difficult when the singular values of the Jacobian associated with each layer are far from 1. Based on these considerations, we propose a new initialization scheme that brings substantially faster convergence.}
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
2453 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2454
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2455 @INPROCEEDINGS{Gori89,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2456 author = {Gori, Marco and Bengio, Yoshua and De Mori, Renato},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2457 title = {BPS: a learning algorithm for capturing the dynamic nature of speech},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2458 booktitle = {International Joint Conference on Neural Networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2459 volume = {2},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2460 year = {1989},
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fsavard
parents:
diff changeset
2461 pages = {417--424},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2462 publisher = {IEEE, New York},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2463 topics={Speech},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2464 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2465
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2466 @INCOLLECTION{Grandvalet+Bengio-ssl-2006,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2467 author = {Grandvalet, Yves and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2468 editor = {Chapelle, Olivier and {Sch{\"{o}}lkopf}, Bernhard and Zien, Alexander},
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parents:
diff changeset
2469 title = {Entropy Regularization},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2470 booktitle = {Semi-Supervised Learning},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2471 year = {2006},
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fsavard
parents:
diff changeset
2472 pages = {151--168},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2473 publisher = {{MIT} Press},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2474 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/entropy_regularization_2006.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2475 abstract = {The problem of semi-supervised induction consists in learning a decision rule from
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2476 labeled and unlabeled data. This task can be undertaken by discriminative methods,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2477 provided that learning criteria are adapted consequently. In this chapter, we motivate the use of entropy regularization as a means to benefit from unlabeled data in
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2478 the framework of maximum a posteriori estimation. The learning criterion is derived
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2479 from clearly stated assumptions and can be applied to any smoothly parametrized
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2480 model of posterior probabilities. The regularization scheme favors low density separation, without any modeling of the density of input features. The contribution
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2481 of unlabeled data to the learning criterion induces local optima, but this problem
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2482 can be alleviated by deterministic annealing. For well-behaved models of posterior
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2483 probabilities, deterministic annealing {EM} provides a decomposition of the learning
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2484 problem in a series of concave subproblems. Other approaches to the semi-supervised
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
2485 problem are shown to be close relatives or limiting cases of entropy regularization.
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fsavard
parents:
diff changeset
2486 A series of experiments illustrates the good behavior of the algorithm in terms of
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2487 performance and robustness with respect to the violation of the postulated low density separation assumption. The minimum entropy solution benefits from unlabeled
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2488 data and is able to challenge mixture models and manifold learning in a number of
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2489 situations.},
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
2490 cat={B},topics={Unsupervised},
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parents:
diff changeset
2491 }
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parents:
diff changeset
2492
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
2493 @INPROCEEDINGS{graves+eck+schmidhuber:bio-adit2004,
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parents:
diff changeset
2494 author = {Graves, A. and Eck, Douglas and Beringer, N. and Schmidhuber, Juergen},
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parents:
diff changeset
2495 title = {Biologically Plausible Speech Recognition with {LSTM} Neural Nets},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2496 booktitle = {Proceedings of the First Int'l Workshop on Biologically Inspired Approaches to Advanced Information Technology (Bio-ADIT)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2497 year = {2004},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2498 pages = {127-136},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2499 url = {http://www.iro.umontreal.ca/~eckdoug/papers/2004_bioadit.pdf},
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fsavard
parents:
diff changeset
2500 abstract = {Long Short-Term Memory ({LSTM}) recurrent neural networks ({RNN}s) are local in space and time and closely related to a biological model of memory in the prefrontal cortex. Not only are they more biologically plausible than previous artificial {RNN}s, they also outperformed them on many artificially generated sequential processing tasks. This encouraged us to apply {LSTM} to more realistic problems, such as the recognition of spoken digits. Without any modification of the underlying algorithm, we achieved results comparable to state-of-the-art Hidden {Markov} Model ({HMM}) based recognisers on both the {TIDIGITS} and TI46 speech corpora. We conclude that {LSTM} should be further investigated as a biologically plausible basis for a bottom-up, neural net-based approach to speech recognition.},
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parents:
diff changeset
2501 source={OwnPublication},
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parents:
diff changeset
2502 sourcetype={Conference},
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parents:
diff changeset
2503 }
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parents:
diff changeset
2504
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2505 @TECHREPORT{graves+eck+schmidhuber:tr-digits2003,
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fsavard
parents:
diff changeset
2506 author = {Graves, A. and Eck, Douglas and Schmidhuber, Juergen},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2507 title = {Comparing {LSTM} Recurrent Networks and Spiking Recurrent Networks on the Recognition of Spoken Digits},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2508 number = {IDSIA-13-03},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2509 year = {2003},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2510 institution = {IDSIA},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2511 abstract = {One advantage of spiking recurrent neural networks ({SNN}s) is an ability to categorise data using a synchrony-based latching mechnanism. This is particularly useful in problems where timewarping is encountered, such as speech recognition. Differentiable recurrent neural networks ({RNN}s) by contrast fail at tasks involving difficult timewarping, despite having sequence learning capabilities superior to {SNN}s. In this paper we demonstrate that Long Short-Term Memory ({LSTM}) is an {RNN} capable of robustly categorizing timewarped speech data, thus combining the most useful features of both paradigms. We compare its performance to {SNN}s on two variants of a spoken digit identification task, using data from an international competition. The first task (described in Nature (Nadis 2003)) required the categorisation of spoken digits with only a single training exemplar, and was specifically designed to test robustness to timewarping. Here {LSTM} performed better than all the {SNN}s in the competition. The second task was to predict spoken digits using a larger training set. Here {LSTM} greatly outperformed an {SNN}-like model found in the literature. These results suggest that {LSTM} has a place in domains that require the learning of large timewarped datasets, such as automatic speech recognition.},
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
2512 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
2513 sourcetype={TechReport},
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parents:
diff changeset
2514 }
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parents:
diff changeset
2515
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
2516 @INPROCEEDINGS{haffner-98,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2517 author = {Haffner, Patrick and Bottou, {L{\'{e}}on} and G. Howard, Paul and Simard, Patrice and Bengio, Yoshua and {LeCun}, Yann},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2518 title = {Browsing through High Quality Document Images with {DjVu}},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2519 booktitle = {Proc. of Advances in Digital Libraries 98},
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
2520 year = {1998},
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
2521 pages = {309--318},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2522 publisher = {IEEE},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2523 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/haffner-98.ps.gz},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2524 topics={HighDimensional},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2525 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2526
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2527 @INPROCEEDINGS{Hamel+al-2009,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2528 author = {Hamel, Philippe and Wood, Sean and Eck, Douglas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2529 title = {Automatic Identification of Instrument Classes in Polyphonic and Poly-Instrument Audio},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2530 booktitle = {10th International Society for Music Information Retrieval Conference},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2531 year = {2009},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2532 pages = {399--404},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2533 url = {http://ismir2009.ismir.net/proceedings/PS3-2.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2534 abstract = {We present and compare several models for automatic identification of instrument classes in polyphonic and poly-instrument audio. The goal is to be able to identify which categories of instrument (Strings, Woodwind, Guitar, Piano, etc.) are present in a given audio example. We use a machine learning approach to solve this task. We constructed a system to generate a large database of musically relevant poly-instrument audio. Our database is generated from hundreds of instruments classified in 7 categories. Musical audio examples are generated by mixing multi-track MIDI files with thousands of instrument combinations. We compare three different classifiers : a Support Vector Machine ({SVM}), a Multilayer Perceptron (MLP) and a Deep Belief Network (DBN). We show that the DBN tends to outperform both the {SVM} and the MLP in most cases.}
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
2535 }
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fsavard
parents:
diff changeset
2536
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2537 @MISC{Hugo+al-snowbird-2007,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2538 author = {Larochelle, Hugo and Bengio, Yoshua and Erhan, Dumitru},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2539 title = {Generalization to a zero-data task: an empirical study},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2540 year = {2007},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2541 howpublished = {Talk and poster presented at the Learning Workshop(Snowbird), San Juan, Puerto Rico, 2007}
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
2542 }
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fsavard
parents:
diff changeset
2543
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
2544 @INPROCEEDINGS{hyper:2000:ijcnn,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2545 author = {Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2546 title = {Continuous Optimization of Hyper-Parameters},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2547 booktitle = {International Joint Conference on Neural Networks 2000},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2548 volume = {I},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2549 year = {2000},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2550 pages = {305--310},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2551 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/hyper-ijcnn2000.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2552 abstract = {Many machine learning algorithms can be formulated as the minimization of a training criterion which involves a hyper-parameter. This hyper-parameter is usually chosen by trial and error with a model selection criterion. In this paper we present a methodology to optimize several hyper-parameters, based on the computation of the gradient of a model selection criterion with respect to the hyper-parameters. In the case of a quadratic training criterion, the gradient of the selection criterion with respect to the hyper-parameters is efficiently computed by back-propagating through a Cholesky decomposition. In the more general case, we show that the implicit function theorem can be used to derive a formula for the hyper-parameter gradient involving second derivatives of the training criterion.},
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
2553 topics={ModelSelection},cat={C},
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fsavard
parents:
diff changeset
2554 }
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fsavard
parents:
diff changeset
2555
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2556 @INPROCEEDINGS{ICML01,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2557 editor = {Brodley, Carla E. and Danyluk, Andrea Pohoreckyj},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2558 title = {Proceedings of the Eighteenth International Conference on Machine Learning (ICML'01)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2559 booktitle = {Proceedings of the Eighteenth International Conference on Machine Learning (ICML'01)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2560 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2561 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2562 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2563
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2564 @INPROCEEDINGS{ICML01-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2565 editor = {Brodley, Carla E. and Danyluk, Andrea Pohoreckyj},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2566 title = {Proceedings of the Eighteenth International Conference on Machine Learning (ICML'01)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2567 booktitle = {ICML'01},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2568 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2569 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2570 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2571
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2572
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2573 @INPROCEEDINGS{ICML02,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2574 editor = {Sammut, Claude and Hoffmann, Achim G.},
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
2575 title = {Proceedings of the Nineteenth International Conference on Machine Learning (ICML'02)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2576 booktitle = {Proceedings of the Nineteenth International Conference on Machine Learning (ICML'02)},
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parents:
diff changeset
2577 year = {-1},
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parents:
diff changeset
2578 publisher = {Morgan Kaufmann}
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fsavard
parents:
diff changeset
2579 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2580
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
2581 @INPROCEEDINGS{ICML02-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2582 editor = {Sammut, Claude and Hoffmann, Achim G.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2583 title = {Proceedings of the Nineteenth International Conference on Machine Learning (ICML'02)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2584 booktitle = {ICML'02},
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fsavard
parents:
diff changeset
2585 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2586 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2587 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2588
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2589
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2590 @INPROCEEDINGS{ICML03,
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parents:
diff changeset
2591 editor = {Fawcett, Tom and Mishra, Nina},
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fsavard
parents:
diff changeset
2592 title = {Proceedings of the Twenty International Conference on Machine Learning (ICML'03)},
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fsavard
parents:
diff changeset
2593 booktitle = {Proceedings of the Twenty International Conference on Machine Learning (ICML'03)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2594 year = {-1},
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parents:
diff changeset
2595 publisher = {AAAI Press}
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parents:
diff changeset
2596 }
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parents:
diff changeset
2597
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
2598 @INPROCEEDINGS{ICML03-short,
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parents:
diff changeset
2599 editor = {Fawcett, Tom and Mishra, Nina},
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fsavard
parents:
diff changeset
2600 title = {Proceedings of the Twenty International Conference on Machine Learning (ICML'03)},
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
2601 booktitle = {ICML'03},
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fsavard
parents:
diff changeset
2602 year = {-1},
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fsavard
parents:
diff changeset
2603 publisher = {AAAI Press}
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parents:
diff changeset
2604 }
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parents:
diff changeset
2605
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2606
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
2607 @INPROCEEDINGS{ICML04,
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parents:
diff changeset
2608 editor = {Brodley, Carla E.},
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fsavard
parents:
diff changeset
2609 title = {Proceedings of the Twenty-first International Conference on Machine Learning (ICML'04)},
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fsavard
parents:
diff changeset
2610 booktitle = {Proceedings of the Twenty-first International Conference on Machine Learning (ICML'04)},
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fsavard
parents:
diff changeset
2611 year = {-1},
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parents:
diff changeset
2612 publisher = {ACM}
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parents:
diff changeset
2613 }
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parents:
diff changeset
2614
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
2615 @INPROCEEDINGS{ICML04-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2616 editor = {Brodley, Carla E.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2617 title = {Proceedings of the Twenty-first International Conference on Machine Learning (ICML'04)},
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parents:
diff changeset
2618 booktitle = {ICML'04},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2619 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2620 publisher = {ACM}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2621 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2622
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2623
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2624 @INPROCEEDINGS{ICML05-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2625 editor = {Raedt, Luc De and Wrobel, Stefan},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2626 title = {Proceedings of the Twenty-second International Conference on Machine Learning (ICML'05)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2627 booktitle = {ICML'05},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2628 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2629 publisher = {ACM}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2630 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2631
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2632
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2633 @INPROCEEDINGS{ICML06-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2634 editor = {Cohen, William W. and Moore, Andrew},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2635 title = {Proceedings of the Twenty-three International Conference on Machine Learning (ICML'06)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2636 booktitle = {ICML'06},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2637 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2638 publisher = {ACM}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2639 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2640
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2641
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2642 @INPROCEEDINGS{ICML07-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2643 editor = {Ghahramani, Zoubin},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2644 title = {Proceedings of the 24th International Conference on Machine Learning (ICML'07)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2645 booktitle = {ICML'07},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2646 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2647 publisher = {ACM}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2648 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2649
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2650
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2651 @INPROCEEDINGS{ICML08-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2652 editor = {Cohen, William W. and McCallum, Andrew and Roweis, Sam T.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2653 title = {Proceedings of the Twenty-fifth International Conference on Machine Learning (ICML'08)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2654 booktitle = {ICML'08},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2655 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2656 publisher = {ACM}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2657 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2658
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2659
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2660 @INPROCEEDINGS{ICML09-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2661 editor = {Bottou, {L{\'{e}}on} and Littman, Michael},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2662 title = {Proceedings of the Twenty-sixth International Conference on Machine Learning (ICML'09)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2663 booktitle = {ICML'09},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2664 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2665 publisher = {ACM}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2666 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2667
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2668
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2669 @INPROCEEDINGS{ICML96,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2670 editor = {Saitta, L.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2671 title = {Proceedings of the Thirteenth International Conference on Machine Learning (ICML'96)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2672 booktitle = {Proceedings of the Thirteenth International Conference on Machine Learning (ICML'96)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2673 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2674 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2675 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2676
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2677 @INPROCEEDINGS{ICML96-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2678 editor = {Saitta, L.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2679 title = {Proceedings of the Thirteenth International Conference on Machine Learning (ICML'96)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2680 booktitle = {ICML'96},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2681 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2682 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2683 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2684
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2685
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2686 @INPROCEEDINGS{ICML97,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2687 editor = {Fisher, Douglas H.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2688 title = {{}Proceedings of the Fourteenth International Conference on Machine Learning (ICML'97)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2689 booktitle = {Proceedings of the Fourteenth International Conference on Machine Learning (ICML'97)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2690 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2691 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2692 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2693
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2694 @INPROCEEDINGS{ICML97-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2695 editor = {Fisher, Douglas H.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2696 title = {{}Proceedings of the Fourteenth International Conference on Machine Learning (ICML'97)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2697 booktitle = {ICML'97},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2698 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2699 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2700 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2701
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2702
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2703 @INPROCEEDINGS{ICML98,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2704 editor = {Shavlik, Jude W.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2705 title = {Proceedings of the Fifteenth International Conference on Machine Learning (ICML'98)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2706 booktitle = {Proceedings of the Fifteenth International Conference on Machine Learning (ICML'98)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2707 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2708 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2709 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2710
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2711 @INPROCEEDINGS{ICML98-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2712 editor = {Shavlik, Jude W.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2713 title = {Proceedings of the Fifteenth International Conference on Machine Learning (ICML'98)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2714 booktitle = {ICML'98},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2715 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2716 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2717 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2718
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2719
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2720 @INPROCEEDINGS{ICML99,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2721 editor = {Bratko, Ivan and Dzeroski, Saso},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2722 title = {Proceedings of the Sixteenth International Conference on Machine Learning (ICML'99)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2723 booktitle = {Proceedings of the Sixteenth International Conference on Machine Learning (ICML'99)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2724 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2725 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2726 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2727
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2728 @INPROCEEDINGS{ICML99-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2729 editor = {Bratko, Ivan and Dzeroski, Saso},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2730 title = {Proceedings of the Sixteenth International Conference on Machine Learning (ICML'99)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2731 booktitle = {ICML'99},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2732 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2733 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2734 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2735
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2736
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2737 @INCOLLECTION{jaeger+eck:2007,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2738 author = {Jaeger, H. and Eck, Douglas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2739 title = {Can't get you out of my head: {A} connectionist model of cyclic rehearsal},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2740 booktitle = {Modeling Communications with Robots and Virtual Humans},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2741 series = {{LNCS}},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2742 year = {2007},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2743 publisher = {Springer-Verlag},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2744 url = {http://www.iro.umontreal.ca/~eckdoug/papers/2007_jaeger_eck.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2745 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2746 sourcetype={Chapter},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2747 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2748
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2749 @MISC{James+al-snowbird-2008,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2750 author = {Bergstra, James and Bengio, Yoshua and Louradour, Jerome},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2751 title = {Image Classification using Higher-Order Neural Models},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2752 year = {2008},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2753 howpublished = {The Learning Workshop (Snowbird, Utah)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2754 url = {http://snowbird.djvuzone.org/2007/abstracts/161.pdf}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2755 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2756
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2757
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2758 @INPROCEEDINGS{Kegl+Bertin+Eck-2008,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2759 author = {K{\'{e}}gl, Bal{\'{a}}zs and Bertin-Mahieux, Thierry and Eck, Douglas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2760 title = {Metropolis-Hastings Sampling in a FilterBoost Music Classifier},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2761 booktitle = {Music and machine learning workshop (ICML08)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2762 year = {2008}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2763 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2764
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2765 @INPROCEEDINGS{kegl2005b,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2766 author = {K{\'{e}}gl, Bal{\'{a}}zs},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2767 title = {Generalization Error and Algorithmic Convergence of Median Boosting.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2768 year = {2005},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2769 crossref = {NIPS17-shorter},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2770 abstract = {We have recently proposed an extension of ADABOOST to regression that uses the median of the base regressors as the final regressor. In this paper we extend theoretical results obtained for ADABOOST to median boosting and to its localized variant. First, we extend recent results on efficient margin maximizing to show that the algorithm can converge to the maximum achievable margin within a preset precision in a finite number of steps. Then we provide confidence-interval-type bounds on the generalization error.}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2771 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2772
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2773 @ARTICLE{lacoste+eck:eurasip,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2774 author = {Lacoste, Alexandre and Eck, Douglas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2775 title = {A Supervised Classification Algorithm For Note Onset Detection},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2776 journal = {EURASIP Journal on Applied Signal Processing},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2777 volume = {2007},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2778 number = {ID 43745},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2779 year = {2007},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2780 pages = {1--13},
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parents:
diff changeset
2781 source={OwnPublication},
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diff changeset
2782 sourcetype={Journal},
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diff changeset
2783 }
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diff changeset
2784
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diff changeset
2785 @MASTERSTHESIS{Lajoie2009,
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parents:
diff changeset
2786 author = {Lajoie, Isabelle},
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parents:
diff changeset
2787 keywords = {apprentissage non-supervis{\'{e}}, architecture profonde, auto-encodeur d{\'{e}}bruiteur, machine de {Boltzmann} restreinte, r{\'{e}}seau de neurones artificiel},
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diff changeset
2788 title = {Apprentissage de repr{\'{e}}sentations sur-compl{\`{e}}tes par entra{\^{\i}}nement d’auto-encodeurs},
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parents:
diff changeset
2789 year = {2009},
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parents:
diff changeset
2790 school = {Universit{\'{e}} de Montr{\'{e}}al},
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parents:
diff changeset
2791 abstract = {Les avanc{\'{e}}s dans le domaine de l’intelligence artificielle, permettent {\`{a}} des syst{\`{e}}mes
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parents:
diff changeset
2792 informatiques de r{\'{e}}soudre des t{\^{a}}ches de plus en plus complexes li{\'{e}}es par exemple {\`{a}}
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parents:
diff changeset
2793 la vision, {\`{a}} la compr{\'{e}}hension de signaux sonores ou au traitement de la langue. Parmi
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parents:
diff changeset
2794 les mod{\`{e}}les existants, on retrouve les R{\'{e}}seaux de Neurones Artificiels (RNA), dont la
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parents:
diff changeset
2795 popularit{\'{e}} a fait un grand bond en avant avec la d{\'{e}}couverte de Hinton et al. [22], soit
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diff changeset
2796 l’utilisation de Machines de {Boltzmann} Restreintes (RBM) pour un pr{\'{e}}-entra{\^{\i}}nement
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parents:
diff changeset
2797 non-supervis{\'{e}} couche apr{\`{e}}s couche, facilitant grandement l’entra{\^{\i}}nement supervis{\'{e}} du
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diff changeset
2798 r{\'{e}}seau {\`{a}} plusieurs couches cach{\'{e}}es (DBN), entra{\^{\i}}nement qui s’av{\'{e}}rait jusqu’alors tr{\`{e}}s
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diff changeset
2799 difficile {\`{a}} r{\'{e}}ussir. Depuis cette d{\'{e}}couverte, des chercheurs ont {\'{e}}tudi{\'{e}} l’efficacit{\'{e}} de nouvelles strat{\'{e}}gies de pr{\'{e}}-entra{\^{\i}}nement, telles que l’empilement d’auto-encodeurs traditionnels (SAE) [5, 38], et l’empilement d’auto-encodeur d{\'{e}}bruiteur (SDAE) [44].
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diff changeset
2800 C’est dans ce contexte qu’a d{\'{e}}but{\'{e}} la pr{\'{e}}sente {\'{e}}tude. Apr{\`{e}}s un bref passage en revue des notions de base du domaine de l’apprentissage machine et des m{\'{e}}thodes de
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diff changeset
2801 pr{\'{e}}-entra{\^{\i}}nement employ{\'{e}}es jusqu’{\`{a}} pr{\'{e}}sent avec les modules RBM, AE et DAE, nous
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diff changeset
2802 avons approfondi notre compr{\'{e}}hension du pr{\'{e}}-entra{\^{\i}}nement de type SDAE, explor{\'{e}} ses
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diff changeset
2803 diff{\'{e}}rentes propri{\'{e}}t{\'{e}}s et {\'{e}}tudi{\'{e}} des variantes de SDAE comme strat{\'{e}}gie d’initialisation
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diff changeset
2804 d’architecture profonde. Nous avons ainsi pu, entre autres choses, mettre en lumi{\`{e}}re
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diff changeset
2805 l’influence du niveau de bruit, du nombre de couches et du nombre d’unit{\'{e}}s cach{\'{e}}es
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2806 sur l’erreur de g{\'{e}}n{\'{e}}ralisation du SDAE. Nous avons constat{\'{e}} une am{\'{e}}lioration de la
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diff changeset
2807 performance sur la t{\^{a}}che supervis{\'{e}}e avec l’utilisation des bruits poivre et sel (PS) et
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parents:
diff changeset
2808 gaussien (GS), bruits s’av{\'{e}}rant mieux justifi{\'{e}}s que celui utilis{\'{e}} jusqu’{\`{a}} pr{\'{e}}sent, soit le
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diff changeset
2809 masque {\`{a}} z{\'{e}}ro (MN). De plus, nous avons d{\'{e}}montr{\'{e}} que la performance profitait d’une
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diff changeset
2810 emphase impos{\'{e}}e sur la reconstruction des donn{\'{e}}es corrompues durant l’entra{\^{\i}}nement
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diff changeset
2811 des diff{\'{e}}rents DAE. Nos travaux ont aussi permis de r{\'{e}}v{\'{e}}ler que le DAE {\'{e}}tait en mesure d’apprendre, sur des images naturelles, des filtres semblables {\`{a}} ceux retrouv{\'{e}}s dans
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diff changeset
2812 les cellules V1 du cortex visuel, soit des filtres d{\'{e}}tecteurs de bordures. Nous aurons par
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diff changeset
2813 ailleurs pu montrer que les repr{\'{e}}sentations apprises du SDAE, compos{\'{e}}es des caract{\'{e}}ristiques ainsi extraites, s’av{\'{e}}raient fort utiles {\`{a}} l’apprentissage d’une machine {\`{a}} vecteurs de
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diff changeset
2814 support ({SVM}) lin{\'{e}}aire ou {\`{a}} noyau gaussien, am{\'{e}}liorant grandement sa performance de
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diff changeset
2815 g{\'{e}}n{\'{e}}ralisation. Aussi, nous aurons observ{\'{e}} que similairement au DBN, et contrairement
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parents:
diff changeset
2816 au SAE, le SDAE poss{\'{e}}dait une bonne capacit{\'{e}} en tant que mod{\`{e}}le g{\'{e}}n{\'{e}}rateur. Nous
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diff changeset
2817 avons {\'{e}}galement ouvert la porte {\`{a}} de nouvelles strat{\'{e}}gies de pr{\'{e}}-entra{\^{\i}}nement et d{\'{e}}couvert le potentiel de l’une d’entre elles, soit l’empilement d’auto-encodeurs rebruiteurs
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diff changeset
2818 (SRAE).}
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diff changeset
2819 }
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diff changeset
2820
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parents:
diff changeset
2821 @INPROCEEDINGS{lamere+eck:ismir2007,
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parents:
diff changeset
2822 author = {Lamere, Paul and Eck, Douglas},
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parents:
diff changeset
2823 editor = {},
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diff changeset
2824 title = {Using 3D Visualizations to Explore and Discover Music},
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parents:
diff changeset
2825 booktitle = {{Proceedings of the 8th International Conference on Music Information Retrieval ({ISMIR} 2007)}},
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parents:
diff changeset
2826 year = {2007},
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parents:
diff changeset
2827 publisher = {},
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parents:
diff changeset
2828 source={OwnPublication},
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parents:
diff changeset
2829 sourcetype={Conference},
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parents:
diff changeset
2830 }
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parents:
diff changeset
2831
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parents:
diff changeset
2832 @ARTICLE{Larochelle+al-2010,
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parents:
diff changeset
2833 author = {Larochelle, Hugo and Bengio, Yoshua and Turian, Joseph},
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parents:
diff changeset
2834 title = {Tractable Multivariate Binary Density Estimation and the Restricted {Boltzmann} Forest},
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parents:
diff changeset
2835 journal = {Neural Computation},
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parents:
diff changeset
2836 year = {2010},
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parents:
diff changeset
2837 note = {To appear}
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parents:
diff changeset
2838 }
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parents:
diff changeset
2839
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parents:
diff changeset
2840 @INPROCEEDINGS{Larochelle+Bengio-2008,
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parents:
diff changeset
2841 author = {Larochelle, Hugo and Bengio, Yoshua},
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parents:
diff changeset
2842 title = {Classification using Discriminative Restricted {B}oltzmann Machines},
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parents:
diff changeset
2843 year = {2008},
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parents:
diff changeset
2844 pages = {536--543},
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parents:
diff changeset
2845 crossref = {ICML08-shorter},
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diff changeset
2846 abstract = {Recently, many applications for Restricted {Boltzmann} Machines (RBMs) have been developed for a large variety of learning problems. However, RBMs are usually used as feature extractors for another learning algorithm or to provide a good initialization
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parents:
diff changeset
2847 for deep feed-forward neural network classifiers, and are not considered as a standalone solution to classification problems. In
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parents:
diff changeset
2848 this paper, we argue that RBMs provide a self-contained framework for deriving competitive non-linear classifiers. We present an evaluation of different learning algorithms for
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diff changeset
2849 RBMs which aim at introducing a discriminative component to RBM training and improve their performance as classifiers. This
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diff changeset
2850 approach is simple in that RBMs are used directly to build a classifier, rather than as a stepping stone. Finally, we demonstrate how discriminative RBMs can also be successfully employed in a semi-supervised setting.}
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diff changeset
2851 }
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diff changeset
2852
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parents:
diff changeset
2853 @INPROCEEDINGS{Larochelle-2009,
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parents:
diff changeset
2854 author = {Larochelle, Hugo and Erhan, Dumitru and Vincent, Pascal},
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diff changeset
2855 title = {Deep Learning using Robust Interdependent Codes},
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diff changeset
2856 booktitle = {Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS 2009)},
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diff changeset
2857 year = {2009},
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diff changeset
2858 pages = {312--319},
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parents:
diff changeset
2859 date = "April 16-18, 2009",
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diff changeset
2860 }
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diff changeset
2861
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parents:
diff changeset
2862 @ARTICLE{Larochelle-jmlr-2009,
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parents:
diff changeset
2863 author = {Larochelle, Hugo and Bengio, Yoshua and Louradour, Jerome and Lamblin, Pascal},
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diff changeset
2864 title = {Exploring Strategies for Training Deep Neural Networks},
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diff changeset
2865 volume = {10},
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parents:
diff changeset
2866 year = {2009},
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parents:
diff changeset
2867 pages = {1--40},
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diff changeset
2868 journal = {Journal of Machine Learning Research},
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diff changeset
2869 abstract = {Deep multi-layer neural networks have many levels of non-linearities allowing them to compactly represent highly non-linear and highly-varying functions. However, until recently it was not clear how to train such deep networks, since gradient-based optimization starting from random initialization often appears to get stuck in poor solutions. Hinton et al. recently proposed a greedy layer-wise unsupervised learning procedure relying on the training algorithm of restricted {Boltzmann} machines (RBM) to initialize the parameters of a deep belief network (DBN), a generative model with many layers of hidden causal variables. This was followed by the proposal of another greedy layer-wise procedure, relying on the usage of autoassociator networks. In the context of the above optimization problem, we study these algorithms empirically to better understand their success. Our experiments confirm the hypothesis that the greedy layer-wise unsupervised training strategy helps the optimization by initializing weights in a region near a good local minimum, but also implicitly acts as a sort of regularization that brings better generalization and encourages internal distributed representations that are high-level abstractions of the input. We also present a series of experiments aimed at evaluating the link between the performance of deep neural networks and practical aspects of their topology, for example, demonstrating cases where the addition of more depth helps. Finally, we empirically explore simple variants of these training algorithms, such as the use of different RBM input unit distributions, a simple way of combining gradient estimators to improve performance, as well as on-line versions of those algorithms.}
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2870 }
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2871
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diff changeset
2872 @PHDTHESIS{Larochelle-PhD-2009,
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diff changeset
2873 author = {Larochelle, Hugo},
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diff changeset
2874 keywords = {apprentissage non-supervis{\'{e}}, architecture profonde, autoassociateur, autoencodeur, machine de {Boltzmann} restreinte, r{\'{e}}seau de neurones artificiel},
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2875 title = {{\'{E}}tude de techniques d'apprentissage non-supervis{\'{e}} pour l'am{\'{e}}lioration de l'entra{\^{\i}}nement supervis{\'{e}} de mod{\`{e}}les connexionnistes},
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diff changeset
2876 year = {2009},
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diff changeset
2877 school = {University of Montr{\'{e}}al},
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diff changeset
2878 abstract = {Le domaine de l'intelligence artificielle a pour objectif le d{\'{e}}veloppement de syst{\`{e}}mes informatiques capables de simuler des comportements normalement associ{\'{e}}s {\`{a}} l'intelligence humaine. On aimerait entre autres pouvoir construire une machine qui puisse
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diff changeset
2879 r{\'{e}}soudre des t{\^{a}}ches li{\'{e}}es {\`{a}} la vision (e.g., la reconnaissance d'objet), au traitement de la langue (e.g., l'identification du sujet d'un texte) ou au traitement de signaux sonores (e.g., la reconnaissance de la parole).
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diff changeset
2880 Une approche d{\'{e}}velopp{\'{e}}e afin de r{\'{e}}soudre ce genre de t{\^{a}}ches est bas{\'{e}}e sur l'apprentissage automatique de mod{\`{e}}les {\`{a}} partir de donn{\'{e}}es {\'{e}}tiquet{\'{e}}es refl{\'{e}}tant le comportement intelligent {\`{a}} {\'{e}}muler. Entre autre, il a {\'{e}}t{\'{e}} propos{\'{e}} de mod{\'{e}}liser le calcul n{\'{e}}cessaire {\`{a}} la
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diff changeset
2881 r{\'{e}}solution d'une t{\^{a}}che {\`{a}} l'aide d'un r{\'{e}}seau de neurones artificiel, dont il est possible d'adapter le comportement {\`{a}} l'aide de la r{\'{e}}tropropagation [99, 131] d'un gradient informatif sur les erreurs commises par le r{\'{e}}seau. Populaire durant les ann{\'{e}}es 80, cette
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diff changeset
2882 approche sp{\'{e}}cifique a depuis perdu partiellement de son attrait, suite au d{\'{e}}veloppement des m{\'{e}}thodes {\`{a}} noyau. Celles-ci sont souvent plus stables, plus faciles {\`{a}} utiliser et leur performance est souvent au moins aussi {\'{e}}lev{\'{e}}e pour une vaste gamme de probl{\`{e}}mes.
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2883 Les m{\'{e}}thodes d'apprentissage automatique ont donc progress{\'{e}} dans leur fonctionnement, mais aussi dans la complexit{\'{e}} des probl{\`{e}}mes auxquels elles se sont attaqu{\'{e}}. Ainsi, plus r{\'{e}}cemment, des travaux [12, 15] ont commenc{\'{e}} {\`{a}} {\'{e}}mettre des doutes sur la capacit{\'{e}} des machines {\`{a}} noyau {\`{a}} pouvoir efficacement r{\'{e}}soudre des probl{\`{e}}mes de la complexit{\'{e}} requise par l'intelligence artificielle. Parall{\`{e}}lement, Hinton et al. [81] faisaient une perc{\'{e}}e dans l'apprentissage automatique de r{\'{e}}seaux de neurones, en proposant une proc{\'{e}}dure permettant l'entra{\^{\i}}nement de r{\'{e}}seaux de neurones d'une plus grande complexit{\'{e}} (i.e., avec plus de couches de neurones cach{\'{e}}es) qu'il n'{\'{e}}tait possible auparavant.
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2884 C'est dans ce contexte qu'ont {\'{e}}t{\'{e}} conduits les travaux de cette th{\`{e}}se. Cette th{\`{e}}se d{\'{e}}bute par une exposition des principes de base de l'apprentissage automatique (chapitre 1) et une discussion des obstacles {\`{a}} l'obtention d'un mod{\`{e}}le ayant une bonne performance
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2885 de g{\'{e}}n{\'{e}}ralisation (chapitre 2). Puis, sont pr{\'{e}}sent{\'{e}}es les contributions apport{\'{e}}es dans le cadre de cinq articles, contributions qui sont toutes bas{\'{e}}es sur l'utilisation d'une certaine
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2886 forme d'apprentissage non-supervis{\'{e}}.
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parents:
diff changeset
2887 Le premier article (chapitre 4) propose une m{\'{e}}thode d'entra{\^{\i}}nement pour un type sp{\'{e}}cifique de r{\'{e}}seau {\`{a}} une seule couche cach{\'{e}}e (la machine de {Boltzmann} restreinte) bas{\'{e}}e sur une combinaison des apprentissages supervis{\'{e}} et non-supervis{\'{e}}. Cette m{\'{e}}thode permet d'obtenir une meilleure performance de g{\'{e}}n{\'{e}}ralisation qu'un r{\'{e}}seau de neurones standard ou qu'une machine {\`{a}} vecteurs de support {\`{a}} noyau, et met en {\'{e}}vidence de fa{\c c}on
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parents:
diff changeset
2888 explicite les b{\'{e}}n{\'{e}}fices qu'apporte l'apprentissage non-supervis{\'{e}} {\`{a}} l'entra{\^{\i}}nement d'un r{\'{e}}seau de neurones.
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parents:
diff changeset
2889 Ensuite, dans le second article (chapitre 6), on {\'{e}}tudie et {\'{e}}tend la proc{\'{e}}dure d'entra{\^{\i}}nement propos{\'{e}}e par Hinton et al. [81]. Plus sp{\'{e}}cifiquement, on y propose une approche diff{\'{e}}rente mais plus flexible pour initialiser un r{\'{e}}seau {\`{a}} plusieurs couches cach{\'{e}}es, bas{\'{e}}e sur un r{\'{e}}seau autoassociateur. On y explore aussi l'impact du nombre de couches et de neurones par couche sur la performance d'un r{\'{e}}seau et on y d{\'{e}}crit diff{\'{e}}rentes variantes mieux adapt{\'{e}}es {\`{a}} l'apprentissage en ligne ou pour donn{\'{e}}es {\`{a}} valeurs continues.
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parents:
diff changeset
2890 Dans le troisi{\`{e}}me article (chapitre 8), on explore plut{\^{o}}t la performance de r{\'{e}}seaux profonds sur plusieurs probl{\`{e}}mes de classification diff{\'{e}}rents. Les probl{\`{e}}mes choisis ont la propri{\'{e}}t{\'{e}} d'avoir {\'{e}}t{\'{e}} g{\'{e}}n{\'{e}}r{\'{e}}s {\`{a}} partir de plusieurs facteurs de variation. Cette propri{\'{e}}t{\'{e}}, qui caract{\'{e}}rise les probl{\`{e}}mes li{\'{e}}s {\`{a}} l'intelligence artificielle, pose difficult{\'{e}} aux machines {\`{a}} noyau, tel que confirm{\'{e}} par les exp{\'{e}}riences de cet article.
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parents:
diff changeset
2891 Le quatri{\`{e}}me article (chapitre 10) pr{\'{e}}sente une am{\'{e}}lioration de l'approche bas{\'{e}}e sur les r{\'{e}}seaux autoassociateurs. Cette am{\'{e}}lioration applique une modification simple {\`{a}} la proc{\'{e}}dure d'entra{\^{\i}}nement d'un r{\'{e}}seau autoassociateur, en « bruitant » les entr{\'{e}}es du r{\'{e}}seau afin que celui-ci soit forc{\'{e}} {\`{a}} la d{\'{e}}bruiter.
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parents:
diff changeset
2892 Le cinqui{\`{e}}me et dernier article (chapitre 12) apporte une autre am{\'{e}}lioration aux r{\'{e}}seaux autoassociateurs, en permettant des interactions d'inhibition ou d'excitation entre les neurones cach{\'{e}}s de ces r{\'{e}}seaux. On y d{\'{e}}montre que de telles interactions peuvent
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fsavard
parents:
diff changeset
2893 {\^{e}}tre apprises et sont b{\'{e}}n{\'{e}}fiques {\`{a}} la performance d'un r{\'{e}}seau profond.}
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parents:
diff changeset
2894 }
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parents:
diff changeset
2895
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parents:
diff changeset
2896 @INPROCEEDINGS{Larochelle2008,
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parents:
diff changeset
2897 author = {Larochelle, Hugo and Erhan, Dumitru and Bengio, Yoshua},
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parents:
diff changeset
2898 title = {Zero-data Learning of New Tasks},
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parents:
diff changeset
2899 booktitle = {AAAI Conference on Artificial Intelligence},
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parents:
diff changeset
2900 year = {2008},
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parents:
diff changeset
2901 url = {http://www-etud.iro.umontreal.ca/~larocheh/publications/aaai2008_zero-data.pdf},
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parents:
diff changeset
2902 abstract = {Recently, many applications for Restricted {Boltzmann} Machines (RBMs) have been developed for a large variety of learning problems. However, RBMs are usually used as feature extractors for another learning algorithm or to provide a good initialization
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parents:
diff changeset
2903 for deep feed-forward neural network classifiers, and are not considered as a standalone solution to classification problems. In
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
2904 this paper, we argue that RBMs provide a self-contained framework for deriving competitive non-linear classifiers. We present an evaluation of different learning algorithms for
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parents:
diff changeset
2905 RBMs which aim at introducing a discriminative component to RBM training and improve their performance as classifiers. This
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parents:
diff changeset
2906 approach is simple in that RBMs are used directly to build a classifier, rather than as a stepping stone. Finally, we demonstrate how discriminative RBMs can also be successfully employed in a semi-supervised setting.}
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parents:
diff changeset
2907 }
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parents:
diff changeset
2908
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parents:
diff changeset
2909 @INPROCEEDINGS{LarochelleH2007,
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parents:
diff changeset
2910 author = {Larochelle, Hugo and Erhan, Dumitru and Courville, Aaron and Bergstra, James and Bengio, Yoshua},
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parents:
diff changeset
2911 title = {An Empirical Evaluation of Deep Architectures on Problems with Many Factors of Variation},
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parents:
diff changeset
2912 year = {2007},
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parents:
diff changeset
2913 pages = {473--480},
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parents:
diff changeset
2914 crossref = {ICML07-shorter},
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parents:
diff changeset
2915 abstract = {Recently, several learning algorithms relying on models with deep architectures have been proposed. Though they have demonstrated impressive performance, to date, they have only been evaluated on relatively simple problems such as digit recognition in a controlled environment, for which many machine learning algorithms already report reasonable results. Here, we present a series of experiments which indicate that these models show promise in solving harder learning problems that exhibit many factors of variation. These models are compared with well-established algorithms such as Support Vector Machines and single hidden-layer feed-forward neural networks.}
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parents:
diff changeset
2916 }
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parents:
diff changeset
2917
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parents:
diff changeset
2918 @MASTERSTHESIS{Latendresse-MSc,
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parents:
diff changeset
2919 author = {Latendresse, Simon},
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parents:
diff changeset
2920 title = {L'utilisation d'hyper-param{\`{e}}tres pour la selection de variables},
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parents:
diff changeset
2921 year = {1999},
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parents:
diff changeset
2922 school = {Universit{\'{e}} de Montreal, Dept. IRO},
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fsavard
parents:
diff changeset
2923 note = {(in French)}
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fsavard
parents:
diff changeset
2924 }
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fsavard
parents:
diff changeset
2925
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
2926 @MASTERSTHESIS{Lauzon99,
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parents:
diff changeset
2927 author = {Lauzon, Vincent-Philippe},
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parents:
diff changeset
2928 title = {Mod{\'{e}}les statistiques comme algorithmes d'apprentissage et {MMCC}s; pr{\'{e}}diction de s{\'{e}}ries financi{\`{e}}res},
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parents:
diff changeset
2929 year = {1999},
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
2930 school = {D{\'{e}}epartement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2931 crossref = {DIRO}
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fsavard
parents:
diff changeset
2932 }
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fsavard
parents:
diff changeset
2933
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
2934 @INPROCEEDINGS{lecun-93,
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fsavard
parents:
diff changeset
2935 author = {{LeCun}, Yann and Bengio, Yoshua and Henderson, Donnie and Weisbuch, A. and Weissman, H. and L., Jackel},
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parents:
diff changeset
2936 title = {On-line handwriting recognition with neural networks: spatial representation versus temporal representation.},
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parents:
diff changeset
2937 booktitle = {Proc. International Conference on handwriting and drawing.},
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parents:
diff changeset
2938 year = {1993},
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parents:
diff changeset
2939 publisher = {Ecole Nationale Superieure des Telecommunications},
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parents:
diff changeset
2940 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/lecun-93.ps.gz},
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parents:
diff changeset
2941 topics={PriorKnowledge,Speech},cat={C},
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parents:
diff changeset
2942 }
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fsavard
parents:
diff changeset
2943
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parents:
diff changeset
2944 @INPROCEEDINGS{lecun-99,
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parents:
diff changeset
2945 author = {{LeCun}, Yann and Haffner, Patrick and Bottou, {L{\'{e}}on} and Bengio, Yoshua},
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parents:
diff changeset
2946 editor = {Forsyth, D.},
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parents:
diff changeset
2947 title = {Object Recognition with Gradient-Based Learning},
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fsavard
parents:
diff changeset
2948 booktitle = {Shape, Contour and Grouping in Computer Vision},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2949 year = {1999},
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parents:
diff changeset
2950 pages = {319-345},
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fsavard
parents:
diff changeset
2951 publisher = {Springer},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2952 url = {orig/lecun-99.ps.gz},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2953 topics={PriorKnowledge,Speech},cat={B},
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fsavard
parents:
diff changeset
2954 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2955
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2956 @TECHREPORT{lecun-99b,
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fsavard
parents:
diff changeset
2957 author = {{LeCun}, Yann and Haffner, Patrick and Bottou, {L{\'{e}}on} and Bengio, Yoshua},
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fsavard
parents:
diff changeset
2958 title = {Gradient-Based Learning for Object Detection, Segmentation and Recognition},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2959 year = {1999},
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fsavard
parents:
diff changeset
2960 institution = {AT\&T Labs},
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fsavard
parents:
diff changeset
2961 url = {orig/lecun-99b.ps.gz},
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fsavard
parents:
diff changeset
2962 topics={Speech},cat={T},
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fsavard
parents:
diff changeset
2963 }
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fsavard
parents:
diff changeset
2964
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fsavard
parents:
diff changeset
2965 @INPROCEEDINGS{lecun-bengio-94,
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parents:
diff changeset
2966 author = {{LeCun}, Yann and Bengio, Yoshua},
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parents:
diff changeset
2967 title = {Word-level training of a handwritten word recognizer based on convolutional neural networks},
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fsavard
parents:
diff changeset
2968 booktitle = {Proc. of the International Conference on Pattern Recognition},
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parents:
diff changeset
2969 volume = {II},
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fsavard
parents:
diff changeset
2970 year = {1994},
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parents:
diff changeset
2971 pages = {88--92},
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parents:
diff changeset
2972 publisher = {IEEE},
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parents:
diff changeset
2973 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/icpr-word.ps},
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parents:
diff changeset
2974 abstract = {We introduce a new approach for on-line recognition of handwritten words written in unconstrained mixed style. Words are represented by low resolution “annotated images” where each pixel contains information about trajectory direction and curvature. The recognizer is a convolution network which can be spatially replicated. From the network output, a hidden {Markov} model produces word scores. The entire system is globally trained to minimize word-level errors.},
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parents:
diff changeset
2975 topics={Speech},cat={C},
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parents:
diff changeset
2976 }
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fsavard
parents:
diff changeset
2977
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fsavard
parents:
diff changeset
2978 @INPROCEEDINGS{lecun-bengio-95a,
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fsavard
parents:
diff changeset
2979 author = {{LeCun}, Yann and Bengio, Yoshua},
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fsavard
parents:
diff changeset
2980 editor = {Arbib, M. A.},
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fsavard
parents:
diff changeset
2981 title = {Convolutional Networks for Images, Speech, and Time-Series},
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fsavard
parents:
diff changeset
2982 booktitle = {The Handbook of Brain Theory and Neural Networks},
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parents:
diff changeset
2983 year = {1995},
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parents:
diff changeset
2984 pages = {255--257},
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fsavard
parents:
diff changeset
2985 publisher = {MIT Press},
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fsavard
parents:
diff changeset
2986 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/handbook-convo.pdf},
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fsavard
parents:
diff changeset
2987 topics={PriorKnowledge,Speech},cat={C},
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fsavard
parents:
diff changeset
2988 }
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fsavard
parents:
diff changeset
2989
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
2990 @INCOLLECTION{lecun-bengio-95b,
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parents:
diff changeset
2991 author = {{LeCun}, Yann and Bengio, Yoshua},
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parents:
diff changeset
2992 editor = {Arbib, M. A.},
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fsavard
parents:
diff changeset
2993 title = {Pattern Recognition and Neural Networks},
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fsavard
parents:
diff changeset
2994 booktitle = {The Handbook of Brain Theory and Neural Networks},
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fsavard
parents:
diff changeset
2995 year = {1995},
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fsavard
parents:
diff changeset
2996 pages = {711--714},
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fsavard
parents:
diff changeset
2997 publisher = {MIT Press},
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fsavard
parents:
diff changeset
2998 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/handbook-patrec.pdf},
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parents:
diff changeset
2999 topics={PriorKnowledge,Speech},cat={B},
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fsavard
parents:
diff changeset
3000 }
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fsavard
parents:
diff changeset
3001
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fsavard
parents:
diff changeset
3002 @ARTICLE{LeCun98,
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parents:
diff changeset
3003 author = {{LeCun}, Yann and Bottou, {L{\'{e}}on} and Bengio, Yoshua and Haffner, Patrick},
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fsavard
parents:
diff changeset
3004 title = {Gradient-Based Learning Applied to Document Recognition},
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fsavard
parents:
diff changeset
3005 journal = {Proceedings of the IEEE},
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fsavard
parents:
diff changeset
3006 volume = {86},
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fsavard
parents:
diff changeset
3007 number = {11},
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parents:
diff changeset
3008 year = {1998},
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fsavard
parents:
diff changeset
3009 pages = {2278--2324},
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fsavard
parents:
diff changeset
3010 abstract = {Multilayer Neural Networks trained with the backpropagation algorithm constitute the best example of a successful Gradient-Based Learning technique. Given an appropriate network architecture, Gradient-Based Learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional Neural Networks, that are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques.
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fsavard
parents:
diff changeset
3011 Real-life document recognition systems are composed or multiple modules including field extraction, segmentation, recognition, and language modeling. A new learning paradigm, called Graph Transformer Networks (GTN), allows such multi-module systems to be trained globally using Gradient-Based methods so as to minimize an overall performance measure.
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fsavard
parents:
diff changeset
3012 Two systems for on-line handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of Graph Transformer Networks.
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parents:
diff changeset
3013 A Graph Transformer Network for reading bank check is also described. It uses Convolutional Neural Network character recognizers combined with global training techniques to provides record accuracy on business and personal checks. It is deployed commercially and reads several million checks per day.},
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parents:
diff changeset
3014 topics={PriorKnowledge,Speech},cat={C},
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fsavard
parents:
diff changeset
3015 }
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parents:
diff changeset
3016
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fsavard
parents:
diff changeset
3017 @INPROCEEDINGS{Lecun_icassp97,
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fsavard
parents:
diff changeset
3018 author = {{LeCun}, Yann and Bottou, {L{\'{e}}on} and Bengio, Yoshua},
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fsavard
parents:
diff changeset
3019 title = {Reading Checks with graph transformer networks},
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fsavard
parents:
diff changeset
3020 booktitle = {International Conference on Acoustics, Speech and Signal Processing},
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fsavard
parents:
diff changeset
3021 volume = {1},
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parents:
diff changeset
3022 year = {1997},
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parents:
diff changeset
3023 pages = {151--154},
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parents:
diff changeset
3024 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/lecun-bottou-bengio-97.ps.gz},
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parents:
diff changeset
3025 topics={Speech},cat={C},
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fsavard
parents:
diff changeset
3026 }
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fsavard
parents:
diff changeset
3027
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fsavard
parents:
diff changeset
3028 @ARTICLE{LeRoux+Bengio-2010,
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fsavard
parents:
diff changeset
3029 author = {Le Roux, Nicolas and Bengio, Yoshua},
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fsavard
parents:
diff changeset
3030 title = {Deep Belief Networks are Compact Universal Approximators},
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parents:
diff changeset
3031 journal = {Neural Computation},
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parents:
diff changeset
3032 year = {2010},
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fsavard
parents:
diff changeset
3033 note = {To appear}
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fsavard
parents:
diff changeset
3034 }
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fsavard
parents:
diff changeset
3035
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fsavard
parents:
diff changeset
3036 @TECHREPORT{LeRoux-Bengio-2007-TR,
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fsavard
parents:
diff changeset
3037 author = {Le Roux, Nicolas and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3038 title = {Representational Power of Restricted {B}oltzmann Machines and Deep Belief Networks},
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fsavard
parents:
diff changeset
3039 number = {1294},
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fsavard
parents:
diff changeset
3040 year = {2007},
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fsavard
parents:
diff changeset
3041 institution = {D{\'{e}}partement d'Informatique et de Recherche Op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
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fsavard
parents:
diff changeset
3042 abstract = {Deep Belief Networks (DBN) are generative neural network models with
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3043 many layers of hidden explanatory factors, recently introduced by Hinton et al.,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3044 along with a greedy layer-wise unsupervised learning algorithm. The building
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3045 block of a DBN is a probabilistic model called a Restricted {Boltzmann} Machine
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3046 (RBM), used to represent one layer of the model. Restricted {Boltzmann} Machines
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3047 are interesting because inference is easy in them, and because they have been
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3048 successfully used as building blocks for training deeper models.
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fsavard
parents:
diff changeset
3049 We first prove that adding hidden units yields strictly improved modeling
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3050 power, while a second theorem shows that RBMs are universal approximators of
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3051 discrete distributions. We then study the question of whether DBNs with more
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3052 layers are strictly more powerful in terms of representational power. This
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3053 suggests a new and less greedy criterion for training RBMs within DBNs.}
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fsavard
parents:
diff changeset
3054 }
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fsavard
parents:
diff changeset
3055
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3056 @ARTICLE{LeRoux-Bengio-2008,
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fsavard
parents:
diff changeset
3057 author = {Le Roux, Nicolas and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3058 title = {Representational Power of Restricted {B}oltzmann Machines and Deep Belief Networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3059 journal = {Neural Computation},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3060 volume = {20},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3061 number = {6},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3062 year = {2008},
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fsavard
parents:
diff changeset
3063 pages = {1631--1649},
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fsavard
parents:
diff changeset
3064 abstract = {Deep Belief Networks (DBN) are generative neural network models with many layers of hidden explanatory factors, recently introduced by Hinton et al., along with a greedy layer-wise unsupervised learning algorithm. The building block of a DBN is a probabilistic model called a Restricted {Boltzmann} Machine (RBM), used to represent one layer of the model. Restricted {Boltzmann} Machines are interesting because inference is easy in them, and because they have been successfully used as building blocks for training deeper models. We first prove that adding hidden units yields strictly improved modelling power, while a second theorem shows that RBMs are universal approximators of discrete distributions. We then study the question of whether DBNs with more layers are strictly more powerful in terms of representational power. This suggests a new and less greedy criterion for training RBMs within DBNs.}
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parents:
diff changeset
3065 }
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fsavard
parents:
diff changeset
3066
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parents:
diff changeset
3067 @INPROCEEDINGS{LeRoux-continuous,
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fsavard
parents:
diff changeset
3068 author = {Le Roux, Nicolas and Bengio, Yoshua},
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fsavard
parents:
diff changeset
3069 title = {Continuous Neural Networks},
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fsavard
parents:
diff changeset
3070 booktitle = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS'07)},
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fsavard
parents:
diff changeset
3071 year = {2007},
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parents:
diff changeset
3072 publisher = {Omnipress},
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fsavard
parents:
diff changeset
3073 abstract = {This article extends neural networks to the case of an uncountable number of hidden units, in several ways. In the first approach proposed, a finite parametrization is possible, allowing gradient-based learning. While having the same number of parameters as an ordinary neural network, its internal structure suggests that it can represent some smooth functions much more compactly. Under mild assumptions, we also find better error bounds than with ordinary neural networks. Furthermore, this parametrization may help reducing the problem of saturation of the neurons. In a second approach, the input-to-hidden weights arefully non-parametric, yielding a kernel machine for which we demonstrate a simple kernel formula. Interestingly, the resulting kernel machine can be made hyperparameter-free and still generalizes in spite of an absence of explicit regularization.}
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parents:
diff changeset
3074 }
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fsavard
parents:
diff changeset
3075
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fsavard
parents:
diff changeset
3076 @PHDTHESIS{LeRoux-PhD-2008,
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fsavard
parents:
diff changeset
3077 author = {Le Roux, Nicolas},
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fsavard
parents:
diff changeset
3078 title = {Avanc{\'{e}}es th{\'{e}}oriques sur la repr{\'{e}}sentation et l'optimisation des r{\'{e}}seaux de neurones},
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fsavard
parents:
diff changeset
3079 year = {2008},
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fsavard
parents:
diff changeset
3080 school = {Universit{\'{e}} de Montr{\'{e}}al},
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fsavard
parents:
diff changeset
3081 abstract = {Les r{\'{e}}seaux de neurones artificiels ont {\'{e}}t{\'{e}} abondamment utilis{\'{e}}s dans la communaut{\'{e}} de l'apprentissage machine depuis les ann{\'{e}}es 80. Bien qu'ils aient {\'{e}}t{\'{e}} {\'{e}}tudi{\'{e}}s pour la premi{\`{e}}re fois il y a cinquante ans par Rosenblatt [68], ils ne furent r{\'{e}}ellement populaires qu'apr{\`{e}}s l'apparition de la r{\'{e}}tropropagation du gradient, en 1986 [71].
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fsavard
parents:
diff changeset
3082 En 1989, il a {\'{e}}t{\'{e}} prouv{\'{e}} [44] qu'une classe sp{\'{e}}cifique de r{\'{e}}seaux de neurones (les r{\'{e}}seaux de neurones {\`{a}} une couche cach{\'{e}}e) {\'{e}}tait suffisamment puissante pour pouvoir approximer presque n'importe quelle fonction avec une pr{\'{e}}cision arbitraire : le th{\'{e}}or{\`{e}}me d'approximation universelle. Toutefois, bien que ce th{\'{e}}or{\`{e}}me e{\^{u}}t pour cons{\'{e}}quence un int{\'{e}}r{\^{e}}t accru pour les r{\'{e}}seaux de neurones, il semblerait qu'aucun effort n'ait {\'{e}}t{\'{e}} fait pour profiter de cette propri{\'{e}}t{\'{e}}.
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parents:
diff changeset
3083 En outre, l'optimisation des r{\'{e}}seaux de neurones {\`{a}} une couche cach{\'{e}}e n'est pas convexe. Cela a d{\'{e}}tourn{\'{e}} une grande partie de la communaut{\'{e}} vers d'autres algorithmes, comme par exemple les machines {\`{a}} noyau (machines {\`{a}} vecteurs de support et r{\'{e}}gression
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parents:
diff changeset
3084 {\`{a}} noyau, entre autres).
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parents:
diff changeset
3085 La premi{\`{e}}re partie de cette th{\`{e}}se pr{\'{e}}sentera les concepts d'apprentissage machine g{\'{e}}n{\'{e}}raux n{\'{e}}cessaires {\`{a}} la compr{\'{e}}hension des algorithmes utilis{\'{e}}s. La deuxi{\`{e}}me partie se focalisera plus sp{\'{e}}cifiquement sur les m{\'{e}}thodes {\`{a}} noyau et les r{\'{e}}seaux de neurones. La troisi{\`{e}}me partie de ce travail visera ensuite {\`{a}} {\'{e}}tudier les limitations des machines {\`{a}} noyaux et {\`{a}} comprendre les raisons pour lesquelles elles sont inadapt{\'{e}}es {\`{a}} certains probl{\`{e}}mes que nous avons {\`{a}} traiter.
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parents:
diff changeset
3086 La quatri{\`{e}}me partie pr{\'{e}}sente une technique permettant d'optimiser les r{\'{e}}seaux de neurones {\`{a}} une couche cach{\'{e}}e de mani{\`{e}}re convexe. Bien que cette technique s'av{\`{e}}re difficilement exploitable pour des probl{\`{e}}mes de grande taille, une version approch{\'{e}}e permet d'obtenir une bonne solution dans un temps raisonnable.
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parents:
diff changeset
3087 La cinqui{\`{e}}me partie se concentre sur les r{\'{e}}seaux de neurones {\`{a}} une couche cach{\'{e}}e infinie. Cela leur permet th{\'{e}}oriquement d'exploiter la propri{\'{e}}t{\'{e}} d'approximation universelle et ainsi d'approcher facilement une plus grande classe de fonctions.
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fsavard
parents:
diff changeset
3088 Toutefois, si ces deux variations sur les r{\'{e}}seaux de neurones {\`{a}} une couche cach{\'{e}}e leur conf{\`{e}}rent des propri{\'{e}}t{\'{e}}s int{\'{e}}ressantes, ces derniers ne peuvent extraire plus que des concepts de bas niveau. Les m{\'{e}}thodes {\`{a}} noyau souffrant des m{\^{e}}mes limites, aucun de
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fsavard
parents:
diff changeset
3089 ces deux types d'algorithmes ne peut appr{\'{e}}hender des probl{\`{e}}mes faisant appel {\`{a}} l'apprentissage de concepts de haut niveau.
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fsavard
parents:
diff changeset
3090 R{\'{e}}cemment sont apparus les Deep Belief Networks [39] qui sont des r{\'{e}}seaux de neurones {\`{a}} plusieurs couches cach{\'{e}}es entra{\^{\i}}n{\'{e}}s de mani{\`{e}}re efficace. Cette profondeur leur permet d'extraire des concepts de haut niveau et donc de r{\'{e}}aliser des t{\^{a}}ches hors
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fsavard
parents:
diff changeset
3091 de port{\'{e}}e des algorithmes conventionnels. La sixi{\`{e}}me partie {\'{e}}tudie des propri{\'{e}}t{\'{e}}s de ces r{\'{e}}seaux profonds.
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3092 Les probl{\`{e}}mes que l'on rencontre actuellement n{\'{e}}cessitent non seulement des algorithmes capables d'extraire des concepts de haut niveau, mais {\'{e}}galement des m{\'{e}}thodes d'optimisation capables de traiter l'immense quantit{\'{e}} de donn{\'{e}}es parfois disponibles, si possible en temps r{\'{e}}el. La septi{\`{e}}me partie est donc la pr{\'{e}}sentation d'une nouvelle technique permettant une optimisation plus rapide.}
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fsavard
parents:
diff changeset
3093 }
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fsavard
parents:
diff changeset
3094
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3095 @ARTICLE{lheureux-04,
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fsavard
parents:
diff changeset
3096 author = {{L'Heureux}, Pierre-Jean and Carreau, Julie and Bengio, Yoshua and Delalleau, Olivier and Yue, Shi Yi},
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fsavard
parents:
diff changeset
3097 title = {Locally Linear Embedding for dimensionality reduction in {QSAR}},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3098 journal = {Journal of Computer-Aided Molecular Design},
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fsavard
parents:
diff changeset
3099 volume = {18},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3100 year = {2004},
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parents:
diff changeset
3101 pages = {475--482},
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fsavard
parents:
diff changeset
3102 abstract = {Current practice in Quantitative Structure Activity Relationship (QSAR) methods usually involves generating a great number of chemical descriptors and then cutting them back with variable selection techniques. Variable selection is an effective method to reduce the dimensionality but may discard some valuable information. This paper introduces Locally Linear Embedding ({LLE}), a local non-linear dimensionality reduction technique, that can statistically discover a low-dimensional representation of the chemical data. {LLE} is shown to create more stable representations than other non-linear dimensionality
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3103 reduction algorithms, and to be capable of capturing non-linearity in chemical data.},
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fsavard
parents:
diff changeset
3104 topics={Bioinformatic},cat={J},
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fsavard
parents:
diff changeset
3105 }
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fsavard
parents:
diff changeset
3106
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3107 @TECHREPORT{lm-TR00,
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fsavard
parents:
diff changeset
3108 author = {Bengio, Yoshua and Ducharme, R{\'{e}}jean and Vincent, Pascal},
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fsavard
parents:
diff changeset
3109 title = {A Neural Probabilistic Language Model},
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fsavard
parents:
diff changeset
3110 number = {1178},
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fsavard
parents:
diff changeset
3111 year = {2000},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3112 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
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parents:
diff changeset
3113 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/TR1178.pdf},
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parents:
diff changeset
3114 abstract = {A goal of statistical language modeling is to learn the joint probability function of sequences of words in a language. This is intrinsically difficult because of the curse of dimensionality: a word sequence on which the model will be tested is likely to be different from all the word sequences seen during training. Traditional but very successful approaches based on n-grams obtain generalization by concatenating very short overlapping sequences seen in the training set. We propose to fight the curse of dimensionality by learning a distributed representation for words which allows each training sentence to inform the model about an exponential number of semantically neighboring sentences. The model learns simultaneously (1) a distributed representation for each word along with (2) the probability function for word sequences, expressed in terms of these representations. Generalization is obtained because a sequence of words that has never been seen before gets high probability if it is made or words that are similar (in the sense of having a nearby representation) to words forming an already seen sentence. Training such large models (with millions of parameters) within a reasonable time is itself a significant challenge. We report on experiments using neural networks for the probability function, showing on two text corpora that the proposed approach very significantly improves on a state-of-the-art trigram model, and that the proposed approach allows to take advantage of much longer contexts.},
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parents:
diff changeset
3115 topics={Markov,Unsupervised,Language},cat={T},
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fsavard
parents:
diff changeset
3116 }
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fsavard
parents:
diff changeset
3117
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fsavard
parents:
diff changeset
3118 @INPROCEEDINGS{Maillet+al-2009,
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parents:
diff changeset
3119 author = {Maillet, Fran{\c c}ois and Eck, Douglas and Desjardins, Guillaume and Lamere, Paul},
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parents:
diff changeset
3120 title = {Steerable Playlist Generation by Learning Song Similarity from Radio Station Playlists},
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fsavard
parents:
diff changeset
3121 booktitle = {Proceedings of the 10th International Conference on Music Information Retrieval},
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fsavard
parents:
diff changeset
3122 year = {2009},
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parents:
diff changeset
3123 url = {http://www-etud.iro.umontreal.ca/~mailletf/papers/ismir09-playlist.pdf},
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parents:
diff changeset
3124 abstract = {This paper presents an approach to generating steerable playlists. We first demonstrate a method for learning song transition probabilities from audio features extracted from songs played in professional radio station playlists. We then show that by using this learnt similarity function as a prior, we are able to generate steerable playlists by choosing the next song to play not simply based on that prior, but on a tag cloud that the user is able to manipulate to express the high-level characteristics of the music he wishes Last.fm, to listen to.}
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parents:
diff changeset
3125 }
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parents:
diff changeset
3126
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parents:
diff changeset
3127 @INPROCEEDINGS{manzagol+bertinmahieux+eck:ismir2008,
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fsavard
parents:
diff changeset
3128 author = {Manzagol, Pierre-Antoine and Bertin-Mahieux, Thierry and Eck, Douglas},
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parents:
diff changeset
3129 title = {On the Use of Sparse Time-Relative Auditory Codes for Music},
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fsavard
parents:
diff changeset
3130 booktitle = {{Proceedings of the 9th International Conference on Music Information Retrieval ({ISMIR} 2008)}},
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parents:
diff changeset
3131 year = {2008},
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parents:
diff changeset
3132 abstract = {Many if not most audio features used in MIR research are inspired by work done in speech recognition and are variations on the spectrogram. Recently, much attention has been given to new representations of audio that are sparse and time-relative. These representations are efficient and able to avoid the time-frequency trade-off of a spectrogram. Yet little work with music streams has been conducted and these features remain mostly unused in the MIR community. In this paper we further explore the use of these features for musical signals. In particular, we investigate their use on realistic music examples (i.e. released commercial music) and their use as input features for supervised learning. Furthermore, we identify three specific issues related to these features which will need to be further addressed in order to obtain the full benefit for MIR applications.},
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parents:
diff changeset
3133 source={OwnPublication},
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parents:
diff changeset
3134 sourcetype={Conference},
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parents:
diff changeset
3135 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3136
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
3137 @MASTERSTHESIS{Manzagol-Msc-2007,
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parents:
diff changeset
3138 author = {Manzagol, Pierre-Antoine},
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fsavard
parents:
diff changeset
3139 key = {Algorithme d'apprentissage, méthode de second ordre, gradient naturel, approximation stochastique},
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fsavard
parents:
diff changeset
3140 title = {TONGA - Un algorithme de gradient naturel pour les probl{\`{e}}mes de grande taille},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3141 year = {2007},
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fsavard
parents:
diff changeset
3142 school = {Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3143 abstract = {Les syst{\`{e}}mes adaptatifs sont confront{\'{e}}s {\`{a}} des donn{\'{e}}es qui {\'{e}}voluent rapidement en quantit{\'{e}} et en complexit{\'{e}}. Les avanc{\'{e}}es mat{\'{e}}rielles de l'informatique ne susent pas {\`{a}} compenser cet essor. Une mise {\`{a}} l'{\'{e}}chelle des techniques d'apprentissage est n{\'{e}}cessaire. D'une part, les mod{\`{e}}les doivent gagner en capacit{\'{e}} de repr{\'{e}}sentation. De l'autre, les algorithmes d'apprentissage doivent devenir plus ecaces.
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fsavard
parents:
diff changeset
3144 Nos travaux se situent dans ce contexte des probl{\`{e}}mes de grande taille et portent sur l'am{\'{e}}lioration des algorithmes d'apprentissage. Deux {\'{e}}l{\'{e}}ments de r{\'{e}}ponse sont d{\'{e}}j{\`{a}} connus. Il s'agit des m{\'{e}}thodes de second ordre et de l'approximation stochastique. Or, les m{\'{e}}thodes de second ordre poss{\`{e}}dent des complexit{\'{e}}s en calculs et en m{\'{e}}moire qui sont prohibitives dans le cadre des probl{\`{e}}mes de grande taille. {\'{E}}galement, il est notoirement dicile de concilier ces m{\'{e}}thodes avec l'approximation stochastique. TONGA est un algorithme d'apprentissage con{\c c}u pour faire face {\`{a}} ces dicult{\'{e}}s. Il s'agit d'une implantation stochastique et adapt{\'{e}}e aux probl{\`{e}}mes de grande taille d'une m{\'{e}}thode de second ordre, le gradient naturel. Dans ce m{\'{e}}moire, nous examinons de pr{\`{e}}s ce nouvel algorithme d'apprentissage en le comparant sur plusieurs probl{\`{e}}mes au gradient stochastique, la technique d'optimisation commun{\'{e}}ment utilis{\'{e}}e dans le cadre des probl{\`{e}}mes de grande taille. Nos exp{\'{e}}riences montrent que TONGA est au moins tout aussi ecace que le gradient stochastique, ce qui est un accomplissement en soit. Dans certains cas, TONGA offre une convergence nettement sup{\'{e}}rieure {\`{a}} celle du gradient stochastique.}
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parents:
diff changeset
3145 }
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parents:
diff changeset
3146
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
3147 @INPROCEEDINGS{matic-94,
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parents:
diff changeset
3148 author = {Matic, N. and Henderson, Donnie and {LeCun}, Yann and Bengio, Yoshua},
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parents:
diff changeset
3149 title = {Pen-based visitor registration system (PENGUIN)},
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fsavard
parents:
diff changeset
3150 booktitle = {Conference Record of the Twenty-Eighth Asilomar Conference on Signals, Systems and Computers},
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parents:
diff changeset
3151 year = {1994},
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parents:
diff changeset
3152 publisher = {IEEE},
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parents:
diff changeset
3153 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/matic-94.tiff},
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parents:
diff changeset
3154 abstract = {We describe a new electronic pen-based visitors registration system (PENGUIN) whose goal is to expand and modernize the visitor sign-in procedure at Bell Laboratories. The system uses a pen-interface (i.e. tablet-display) in what is essentially a form filling application. Our pen-interface is coupled with a powerful and accurate on-line handwriting recognition module. A database of AT&T employees (the visitors' hosts) and country names is used to check the recognition module outputs, in order to find the best match. The system provides assistance to the guard at one of the guard stations in routing visitors to their hosts. All the entered data are stored electronically. Initial testing shows that PENGUIN system performs reliably and with high accuracy. It retrieves the correct host name with 97\% accuracy and the correct visitors citizenship with 99\% accuracy. The system is robust and easy to use for both visitors and guards},
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parents:
diff changeset
3155 topics={Speech},cat={C},
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fsavard
parents:
diff changeset
3156 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3157
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
3158 @UNPUBLISHED{mirex2005artist,
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parents:
diff changeset
3159 author = {Bergstra, James and Casagrande, Norman and Eck, Douglas},
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
3160 title = {Artist Recognition: A Timbre- and Rhythm-Based Multiresolution Approach},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3161 year = {2005},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3162 note = {{MIREX} artist recognition contest},
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fsavard
parents:
diff changeset
3163 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3164 sourcetype={Other},
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fsavard
parents:
diff changeset
3165 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3166
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3167 @UNPUBLISHED{mirex2005genre,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3168 author = {Bergstra, James and Casagrande, Norman and Eck, Douglas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3169 title = {Genre Classification: Timbre- and Rhythm-Based Multiresolution Audio Classification},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3170 year = {2005},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3171 note = {{MIREX} genre classification contest},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3172 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3173 sourcetype={Other},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3174 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3175
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3176 @UNPUBLISHED{mirex2005note,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3177 author = {Lacoste, Alexandre and Eck, Douglas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3178 title = {Onset Detection with Artificial Neural Networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3179 year = {2005},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3180 note = {{MIREX} note onset detection contest},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3181 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3182 sourcetype={Other},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3183 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3184
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3185 @UNPUBLISHED{mirex2005tempo,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3186 author = {Eck, Douglas and Casagrande, Norman},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3187 title = {A Tempo-Extraction Algorithm Using an Autocorrelation Phase Matrix and Shannon Entropy},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3188 year = {2005},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3189 note = {{MIREX} tempo extraction contest (www.music-ir.org/\-evaluation/\-mirex-results)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3190 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3191 sourcetype={Other},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3192 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3193
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3194 @INPROCEEDINGS{mitacs-insurance01,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3195 author = {Bengio, Yoshua and Chapados, Nicolas and Dugas, Charles and Ghosn, Joumana and Takeuchi, Ichiro and Vincent, Pascal},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3196 title = {High-Dimensional Data Inference for Automobile Insurance Premia Estimation},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3197 booktitle = {Presented at the 2001 MITACS Annual Meeting},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3198 year = {2001},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3199 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/mitacs_insurance.ps},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3200 topics={HighDimensional,Mining},cat={C},
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fsavard
parents:
diff changeset
3201 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3202
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3203 @INPROCEEDINGS{Morin+al-2005,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3204 author = {Morin, Frederic and Bengio, Yoshua},
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parents:
diff changeset
3205 editor = {Cowell, Robert G. and Ghahramani, Zoubin},
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fsavard
parents:
diff changeset
3206 title = {Hierarchical Probabilistic Neural Network Language Model},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3207 booktitle = {Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3208 year = {2005},
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fsavard
parents:
diff changeset
3209 pages = {246--252},
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fsavard
parents:
diff changeset
3210 publisher = {Society for Artificial Intelligence and Statistics},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3211 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/hierarchical-nnlm-aistats05.pdf},
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parents:
diff changeset
3212 abstract = {In recent years, variants of a neural network architecture for statistical language modeling have been proposed and successfully applied, e.g. in the language modeling component of speech recognizers. The main advantage of these architectures is that they learn an embedding for words (or other symbols) in a continuous space that helps to smooth the language model and provide good generalization even when the number of training examples is insufficient. However, these models are extremely slow in comparison to the more commonly used n-gram models, both for training and recognition. As an alternative to an importance sampling method proposed to speed-up training, we introduce a hierarchical decomposition of the conditional probabilities that yields a speed-up of about 200 both during training and recognition. The hierarchical decomposition is a binary hierarchical clustering constrained by the prior knowledge extracted from the WordNet semantic hierarchy.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3213 topics={Language},cat={C},
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fsavard
parents:
diff changeset
3214 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3215
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3216 @TECHREPORT{Nadeau-inference-TR99,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3217 author = {Nadeau, Claude and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3218 title = {Inference and the Generalization Error},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3219 number = {99s-45},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3220 year = {1999},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3221 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
3222 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/techrep.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
3223 abstract = {We perform a theoretical investigation of the variance of the cross-validation estimate of the generalization error that takes into account the variability due to the choice of training sets and test examples. This allows us to propose two new estimators of this variance. We show, via simulations, that these new statistics perform well relative to the statistics considered in (Dietterich, 1998). In particular, tests of hypothesis based on these don’t tend to be too liberal like other tests currently available, and have good power.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3224 topics={Comparative},cat={T},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3225 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3226
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3227 @INPROCEEDINGS{nadeau:2000:nips,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3228 author = {Nadeau, Claude and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3229 title = {Inference for the Generalization Error},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3230 year = {2000},
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fsavard
parents:
diff changeset
3231 pages = {307--313},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3232 crossref = {NIPS12-shorter},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3233 abstract = {In order to to compare learning algorithms, experimental results reported in the machine learning litterature often use statistical tests of significance. Unfortunately, most of these tests do not take into account the variability due to the choice of training set. We perform a theoretical investigation of the variance of the cross-validation estimate of the generalization error that takes into account the variability due to the choice of training sets. This allows us to propose two new ways to estimate this variance. We show, via simulations, that these new statistics perform well relative to the statistics considered by Dietterich (Dietterich, 1998).},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3234 topics={Comparative},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3235 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3236
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3237 @ARTICLE{nadeau:2001,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3238 author = {Nadeau, Claude and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3239 title = {Inference for the Generalization Error},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3240 journal = {Machine Learning},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3241 year = {2001},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3242 abstract = {In order to compare learning algorithms, experimental results reported in the machine learning literature often use statistical tests of significance to support the claim that a new learning algorithm generalizes better. Such tests should take into account the variability due to the choice of training set and not only that due to the test examples, as is often the case. This could lead to gross underestimation of the variance of the cross-validation estimator, and to the wrong conclusion that the new algorithm is significantly better when it is not. We perform a theoretical investigation of the variance of a cross-validation estimator of the generalization error that takes into account the variability due to the randomness of the training set as well as test examples. Our analysis shows that all the variance estimators that are based only on the results of the cross-validation experiment must be biased. This analysis allows us to propose new estimators of this variance. We show, via simulations, that tests of hypothesis about the generalization error using those new variance estimators have better properties than tests involving variance estimators currently in use and listed in (Dietterich, 1998). In particular, the new tests have correct size and good power. That is, the new tests do not reject the null hypothesis too often when the hypothesis is true, but they tend to frequently reject the null hypothesis when the latter is false.},
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
3243 topics={Comparative},cat={J},
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parents:
diff changeset
3244 }
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fsavard
parents:
diff changeset
3245
b1be957dd1be Added mlj_submission to group every file needed for that.
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parents:
diff changeset
3246 @ARTICLE{NC06,
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parents:
diff changeset
3247 author = {Bengio, Yoshua and Monperrus, Martin and Larochelle, Hugo},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3248 title = {Nonlocal Estimation of Manifold Structure},
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fsavard
parents:
diff changeset
3249 journal = {Neural Computation},
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fsavard
parents:
diff changeset
3250 volume = {18},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3251 year = {2006},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3252 pages = {2509--2528},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3253 abstract = {We claim and present arguments to the effect that a large class of manifold
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3254 learning algorithms that are essentially local and can be framed as
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3255 kernel learning algorithms will suffer from the curse of dimensionality, at
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3256 the dimension of the true underlying manifold. This observation suggests
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3257 to explore non-local manifold learning algorithms which attempt to discover
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3258 shared structure in the tangent planes at different positions. A criterion for
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3259 such an algorithm is proposed and experiments estimating a tangent plane
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3260 prediction function are presented, showing its advantages with respect to
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3261 local manifold learning algorithms: it is able to generalize very far from
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3262 training data (on learning handwritten character image rotations), where a
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3263 local non-parametric method fails.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3264 topics={HighDimensional,Kernel,Unsupervised},cat={J},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3265 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3266
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3267 @INPROCEEDINGS{NIPS1-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3268 editor = {Touretzky, D. S.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3269 title = {Advances in Neural Information Processing Systems 1 (NIPS'88)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3270 booktitle = {NIPS 1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3271 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3272 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3273 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3274
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3275
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3276 @INPROCEEDINGS{NIPS10-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3277 editor = {Jordan, M.I. and Kearns, M.J. and Solla, S.A.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3278 title = {Advances in Neural Information Processing Systems 10 (NIPS'97)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3279 booktitle = {NIPS 10},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3280 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3281 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3282 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3283
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3284
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3285 @INPROCEEDINGS{NIPS11,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3286 editor = {Kearns, M.J. and Solla, S.A.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3287 title = {Advances in Neural Information Processing Systems 11 (NIPS'98)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3288 booktitle = {Advances in Neural Information Processing Systems 11 (NIPS'98)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3289 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3290 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3291 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3292
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3293 @INPROCEEDINGS{NIPS11-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3294 editor = {Kearns, M.J. and Solla, S.A.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3295 title = {Advances in Neural Information Processing Systems 11 (NIPS'98)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3296 booktitle = {NIPS 11},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3297 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3298 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3299 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3300
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3301
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3302 @INPROCEEDINGS{NIPS12-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3303 editor = {Solla, S.A. and Leen, T. K.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3304 title = {Advances in Neural Information Processing Systems 12 (NIPS'99)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3305 booktitle = {NIPS 12},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3306 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3307 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3308 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3309
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3310
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3311 @INPROCEEDINGS{NIPS13-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3312 editor = {Leen, T. K. and Dietterich, T.G.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3313 title = {Advances in Neural Information Processing Systems 13 (NIPS'00)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3314 booktitle = {NIPS 13},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3315 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3316 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3317 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3318
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3319
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3320 @INPROCEEDINGS{NIPS14,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3321 editor = {Dietterich, T.G. and Becker, S. and Ghahramani, Zoubin},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3322 title = {Advances in Neural Information Processing Systems 14 (NIPS'01)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3323 booktitle = {Advances in Neural Information Processing Systems 14 (NIPS'01)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3324 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3325 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3326 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3327
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3328 @INPROCEEDINGS{NIPS14-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3329 editor = {Dietterich, T.G. and Becker, S. and Ghahramani, Zoubin},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3330 title = {Advances in Neural Information Processing Systems 14 (NIPS'01)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3331 booktitle = {NIPS 14},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3332 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3333 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3334 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3335
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3336
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3337 @INPROCEEDINGS{NIPS15-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3338 editor = {Becker, S. and Thrun, Sebastian},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3339 title = {Advances in Neural Information Processing Systems 15 (NIPS'02)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3340 booktitle = {NIPS 15},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3341 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3342 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3343 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3344
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3345
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3346 @INPROCEEDINGS{NIPS16-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3347 editor = {Becker, S. and Saul, L. and {Sch{\"{o}}lkopf}, Bernhard},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3348 title = {Advances in Neural Information Processing Systems 16 (NIPS'03)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3349 booktitle = {NIPS 16},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3350 year = {-1}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3351 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3352
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3353
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3354 @INPROCEEDINGS{NIPS17-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3355 editor = {Saul, Lawrence K. and Weiss, Yair and Bottou, {L{\'{e}}on}},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3356 title = {Advances in Neural Information Processing Systems 17 (NIPS'04)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3357 booktitle = {NIPS 17},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3358 year = {-1}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3359 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3360
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3361
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3362 @INPROCEEDINGS{NIPS18-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3363 editor = {Weiss, Yair and {Sch{\"{o}}lkopf}, Bernhard and Platt, John},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3364 title = {Advances in Neural Information Processing Systems 18 (NIPS'05)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3365 booktitle = {NIPS 18},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3366 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3367 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3368 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3369
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3370
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3371 @INPROCEEDINGS{NIPS19-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3372 editor = {{Sch{\"{o}}lkopf}, Bernhard and Platt, John and Hoffman, Thomas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3373 title = {Advances in Neural Information Processing Systems 19 (NIPS'06)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3374 booktitle = {NIPS 19},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3375 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3376 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3377 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3378
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3379
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3380 @INPROCEEDINGS{NIPS2-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3381 editor = {Touretzky, D. S.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3382 title = {Advances in Neural Information Processing Systems 2 (NIPS'89)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3383 booktitle = {NIPS 2},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3384 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3385 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3386 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3387
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3388
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3389 @INPROCEEDINGS{NIPS20-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3390 editor = {Platt, John and Koller, D. and Singer, Yoram and Roweis, S.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3391 title = {Advances in Neural Information Processing Systems 20 (NIPS'07)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3392 booktitle = {NIPS 20},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3393 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3394 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3395 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3396
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3397
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3398 @INPROCEEDINGS{NIPS2003_AA65,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3399 author = {Bengio, Yoshua and Grandvalet, Yves},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3400 keywords = {cross validation, error bars, generalization error inference, k-fold cross-validation, model selection, statistical comparison of algorithms, variance estimate},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3401 title = {No Unbiased Estimator of the Variance of K-Fold Cross-Validation},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3402 year = {2004},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3403 publisher = {MIT Press},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3404 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/var-kfold-part1-nips.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3405 crossref = {NIPS16},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3406 abstract = {Most machine learning researchers perform quantitative experiments to estimate generalization error and compare algorithm performances. In order to draw statistically convincing conclusions, it is important to estimate the uncertainty of such estimates. This paper studies the estimation of uncertainty around the K-fold cross-validation estimator. The main theorem shows that there exists no universal unbiased estimator of the variance of K-fold cross-validation. An analysis based on the eigendecomposition of the covariance matrix of errors helps to better understand the nature of the problem and shows that naive estimators may grossly underestimate variance, as confirmed by numerical experiments.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3407 topics={Comparative},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3408 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3409
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3410 @INCOLLECTION{NIPS2005_424,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3411 author = {Bengio, Yoshua and Delalleau, Olivier and Le Roux, Nicolas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3412 title = {The Curse of Highly Variable Functions for Local Kernel Machines},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3413 year = {2006},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3414 pages = {107--114},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3415 crossref = {NIPS18-shorter},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3416 abstract = {We present a series of theoretical arguments supporting the claim that a
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3417 large class of modern learning algorithms that rely solely on the smoothness
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3418 prior – with similarity between examples expressed with a local
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3419 kernel – are sensitive to the curse of dimensionality, or more precisely
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3420 to the variability of the target. Our discussion covers supervised, semisupervised
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3421 and unsupervised learning algorithms. These algorithms are
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3422 found to be local in the sense that crucial properties of the learned function
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3423 at x depend mostly on the neighbors of x in the training set. This
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3424 makes them sensitive to the curse of dimensionality, well studied for
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3425 classical non-parametric statistical learning. We show in the case of the
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3426 Gaussian kernel that when the function to be learned has many variations,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3427 these algorithms require a number of training examples proportional to
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3428 the number of variations, which could be large even though there may exist
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3429 short descriptions of the target function, i.e. their Kolmogorov complexity
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3430 may be low. This suggests that there exist non-local learning
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3431 algorithms that at least have the potential to learn about such structured
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3432 but apparently complex functions (because locally they have many variations),
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3433 while not using very specific prior domain knowledge.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3434 topics={HighDimensional,Kernel,Unsupervised},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3435 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3436
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3437 @INPROCEEDINGS{NIPS2005_456,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3438 author = {K{\'{e}}gl, Bal{\'{a}}zs and Wang, Ligen},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3439 title = {Boosting on Manifolds: Adaptive Regularization of Base Classifiers},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3440 year = {2005},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3441 pages = {665--672},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3442 crossref = {NIPS17-shorter},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3443 abstract = {In this paper we propose to combine two powerful ideas, boosting and manifold learning. On the one hand, we improve ADABOOST by incorporating knowledge on the structure of the data into base classifier design and selection. On the other hand, we use ADABOOST’s efficient learning mechanism to significantly improve supervised and semi-supervised algorithms proposed in the context of manifold learning. Beside the specific manifold-based penalization, the resulting algorithm also accommodates the boosting of a large family of regularized learning algorithms.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3444 topics={Boosting},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3445 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3446
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3447 @INCOLLECTION{NIPS2005_519,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3448 author = {Grandvalet, Yves and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3449 title = {Semi-supervised Learning by Entropy Minimization},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3450 year = {2005},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3451 pages = {529--236},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3452 crossref = {NIPS17-shorter},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3453 abstract = {We consider the semi-supervised learning problem, where a decision rule is to be learned from labeled and unlabeled data. In this framework, we motivate minimum entropy regularization, which enables to incorporate unlabeled data in the standard supervised learning. Our approach includes other approaches to the semi-supervised problem as particular or limiting cases. A series of experiments illustrates that the proposed solution benefits from unlabeled data. The method challenges mixture models when the data are sampled from the distribution class spanned by the generative model. The performances are definitely in favor of minimum entropy regularization when generative models are misspecified, and the weighting of unlabeled data provides robustness to the violation of the “cluster assumption”. Finally, we also illustrate that the method can also be far superior to manifold learning in high dimension spaces.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3454 topics={Unsupervised},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3455 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3456
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3457 @INPROCEEDINGS{NIPS2005_539,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3458 author = {Bengio, Yoshua and Larochelle, Hugo and Vincent, Pascal},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3459 title = {Non-Local Manifold Parzen Windows},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3460 year = {2006},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3461 crossref = {NIPS18-shorter},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3462 abstract = {To escape from the curse of dimensionality, we claim that one can learn
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3463 non-local functions, in the sense that the value and shape of the learned
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3464 function at x must be inferred using examples that may be far from x.
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3465 With this objective, we present a non-local non-parametric density estimator.
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3466 It builds upon previously proposed Gaussian mixture models with
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3467 regularized covariance matrices to take into account the local shape of
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3468 the manifold. It also builds upon recent work on non-local estimators of
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3469 the tangent plane of a manifold, which are able to generalize in places
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3470 with little training data, unlike traditional, local, non-parametric models.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3471 topics={HighDimensional,Kernel,Unsupervised},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3472 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3473
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3474 @INPROCEEDINGS{NIPS2005_583,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3475 author = {Bengio, Yoshua and Le Roux, Nicolas and Vincent, Pascal and Delalleau, Olivier and Marcotte, Patrice},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3476 title = {Convex Neural Networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3477 year = {2006},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3478 pages = {123--130},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3479 crossref = {NIPS18-shorter},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3480 abstract = {Convexity has recently received a lot of attention in the machine learning
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3481 community, and the lack of convexity has been seen as a major disadvantage
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3482 of many learning algorithms, such as multi-layer artificial neural
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3483 networks. We show that training multi-layer neural networks in which the
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3484 number of hidden units is learned can be viewed as a convex optimization
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3485 problem. This problem involves an infinite number of variables, but can be
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3486 solved by incrementally inserting a hidden unit at a time, each time finding
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3487 a linear classifier that minimizes a weighted sum of errors.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3488 topics={Boosting},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3489 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3490
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3491 @INPROCEEDINGS{NIPS2005_663,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3492 author = {Rivest, Fran{\c c}ois and Bengio, Yoshua and Kalaska, John},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3493 title = {Brain Inspired Reinforcement Learning},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3494 year = {2005},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3495 pages = {1129--1136},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3496 crossref = {NIPS17-shorter},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3497 abstract = {Successful application of reinforcement learning algorithms often involves considerable hand-crafting of the necessary non-linear features to reduce the complexity of the value functions and hence to promote convergence of the algorithm. In contrast, the human brain readily and autonomously finds the complex features when provided with sufficient training. Recent work in machine learning and neurophysiology has demonstrated the role of the basal ganglia and the frontal cortex in mammalian reinforcement learning. This paper develops and explores new reinforcement learning algorithms inspired by neurological evidence that provides potential new approaches to the feature construction problem. The algorithms are compared and evaluated on the Acrobot task.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3498 topics={BioRules},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3499 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3500
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3501 @INCOLLECTION{NIPS2005_691,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3502 author = {Bengio, Yoshua and Monperrus, Martin},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3503 title = {Non-Local Manifold Tangent Learning},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3504 year = {2005},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3505 pages = {129--136},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3506 crossref = {NIPS17-shorter},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3507 abstract = {We claim and present arguments to the effect that a large class of manifold learning algorithms that are essentially local and can be framed as kernel learning algorithms will suffer from the curse of dimensionality, at the dimension of the true underlying manifold. This observation suggests to explore non-local manifold learning algorithms which attempt to discover shared structure in the tangent planes at different positions. A criterion for such an algorithm is proposed and experiments estimating a tangent plane prediction function are presented, showing its advantages with respect to local manifold learning algorithms: it is able to generalize very far from training data (on learning handwritten character image rotations), where a local non-parametric method fails.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3508 topics={HighDimensional,Unsupervised},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3509 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3510
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3511 @INPROCEEDINGS{NIPS2005_874,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3512 author = {K{\'{e}}gl, Bal{\'{a}}zs},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3513 title = {Generalization Error and Algorithmic Convergence of Median Boosting},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3514 year = {2005},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3515 pages = {657--664},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3516 crossref = {NIPS17-shorter},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3517 abstract = {We have recently proposed an extension of ADABOOST to regression that uses the median of the base regressors as the final regressor. In this paper we extend theoretical results obtained for ADABOOST to median boosting and to its localized variant. First, we extend recent results on efficient margin maximizing to show that the algorithm can converge to the maximum achievable margin within a preset precision in a finite number of steps. Then we provide confidence-interval-type bounds on the generalization error.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3518 topics={Boosting},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3519 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3520
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3521 @INPROCEEDINGS{NIPS2007-56,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3522 author = {Le Roux, Nicolas and Manzagol, Pierre-Antoine and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3523 title = {Topmoumoute online natural gradient algorithm},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3524 year = {2008},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3525 crossref = {NIPS20-shorter},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3526 abstract = {Guided by the goal of obtaining an optimization algorithm that is both fast and yielding good generalization, we study the descent direction maximizing the decrease in generalization error or the probability of not increasing generalization error. The surprising result is that from both the Bayesian and frequentist perspectives this can yield the natural gradient direction. Although that direction can be very expensive to compute we develop an efficient, general, online approximation to the natural gradient descent which is suited to large scale problems. We report experimental results showing much faster convergence in computation time and in number of iterations with TONGA (Topmoumoute Online natural Gradient Algorithm) than with stochastic gradient descent, even on very large datasets.}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3527 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3528
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3529 @INPROCEEDINGS{NIPS2007-812,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3530 author = {Chapados, Nicolas and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3531 title = {Augmented Functional Time Series Representation and Forecasting with Gaussian Processes},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3532 year = {2008},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3533 pages = {265--272},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3534 crossref = {NIPS20-shorter},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3535 abstract = {We introduce a functional representation of time series which allows forecasts to be performed over an unspecified horizon with progressively-revealed information sets. By virtue of using Gaussian processes, a complete covariance matrix between forecasts at several time-steps is available. This information is put to use in an application to actively trade price spreads between commodity futures contracts. The approach delivers impressive out-of-sample risk-adjusted returns after transaction costs on a portfolio of 30 spreads.}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3536 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3537
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3538 @INPROCEEDINGS{NIPS2007-925,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3539 author = {Le Roux, Nicolas and Bengio, Yoshua and Lamblin, Pascal and Joliveau, Marc and K{\'{e}}gl, Bal{\'{a}}zs},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3540 title = {Learning the 2-D Topology of Images},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3541 year = {2008},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3542 pages = {841--848},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3543 crossref = {NIPS20-shorter},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3544 abstract = {We study the following question: is the two-dimensional structure of images a very strong prior or is it something that can be learned with a few examples of natural images? If someone gave us a learning task involving images for which the two-dimensional topology of pixels was not known, could we discover it automatically and exploit it? For example suppose that the pixels had been permuted in a fixed but unknown way, could we recover the relative two-dimensional location of pixels on images? The surprising result presented here is that not only the answer is yes but that about as few as a thousand images are enough to approximately recover the relative locations of about a thousand pixels. This is achieved using a manifold learning algorithm applied to pixels associated with a measure of distributional similarity between pixel intensities. We compare different topologyextraction approaches and show how having the two-dimensional topology can be exploited.}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3545 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3546
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3547 @INPROCEEDINGS{NIPS21,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3548 editor = {Koller, D. and Schuurmans, Dale and Bengio, Yoshua and Bottou, {L{\'{e}}on}},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3549 title = {Advances in Neural Information Processing Systems 21 (NIPS'08)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3550 booktitle = {Advances in Neural Information Processing Systems 21 (NIPS'08)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3551 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3552 publisher = {Nips Foundation (http://books.nips.cc)}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3553 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3554
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3555 @INPROCEEDINGS{NIPS21-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3556 editor = {Koller, D. and Schuurmans, Dale and Bengio, Yoshua and Bottou, {L{\'{e}}on}},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3557 title = {Advances in Neural Information Processing Systems 21 (NIPS'08)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3558 booktitle = {NIPS 21},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3559 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3560 publisher = {Nips Foundation (http://books.nips.cc)}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3561 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3562
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3563
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3564 @INPROCEEDINGS{NIPS22-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3565 editor = {Bengio, Yoshua and Schuurmans, Dale and Williams, Christopher and Lafferty, John and Culotta, Aron},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3566 title = {Advances in Neural Information Processing Systems 22 (NIPS'09)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3567 booktitle = {NIPS 22},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3568 year = {-1}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3569 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3570
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3571
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3572 @INPROCEEDINGS{NIPS3,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3573 editor = {Lipmann, R. P. and Moody, J. E. and Touretzky, D. S.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3574 title = {Advances in Neural Information Processing Systems 3 (NIPS'90)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3575 booktitle = {Advances in Neural Information Processing Systems 3 (NIPS'90)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3576 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3577 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3578 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3579
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3580 @INPROCEEDINGS{NIPS3-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3581 editor = {Lipmann, R. P. and Moody, J. E. and Touretzky, D. S.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3582 title = {Advances in Neural Information Processing Systems 3 (NIPS'90)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3583 booktitle = {NIPS 3},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3584 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3585 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3586 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3587
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3588
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3589 @INPROCEEDINGS{NIPS4-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3590 editor = {Moody, J. E. and Hanson, S. J. and Lipmann, R. P.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3591 title = {Advances in Neural Information Processing Systems 4 (NIPS'91)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3592 booktitle = {NIPS 4},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3593 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3594 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3595 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3596
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3597
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3598 @INPROCEEDINGS{NIPS5,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3599 editor = {Giles, C.L. and Hanson, S. J. and Cowan, J. D.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3600 title = {Advances in Neural Information Processing Systems 5 (NIPS'92)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3601 booktitle = {Advances in Neural Information Processing Systems 5 (NIPS'92)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3602 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3603 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3604 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3605
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3606 @INPROCEEDINGS{NIPS5-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3607 editor = {Giles, C.L. and Hanson, S. J. and Cowan, J. D.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3608 title = {Advances in Neural Information Processing Systems 5 (NIPS'92)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3609 booktitle = {NIPS 5},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3610 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3611 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3612 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3613
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3614
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3615 @INPROCEEDINGS{NIPS6-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3616 editor = {Cowan, J. D. and Tesauro, G. and Alspector, J.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3617 title = {Advances in Neural Information Processing Systems 6 (NIPS'93)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3618 booktitle = {NIPS 6},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3619 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3620 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3621 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3622
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3623
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3624 @INPROCEEDINGS{NIPS7-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3625 editor = {Tesauro, G. and Touretzky, D. S. and Leen, T. K.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3626 title = {Advances in Neural Information Processing Systems 7 (NIPS'94)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3627 booktitle = {NIPS 7},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3628 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3629 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3630 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3631
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3632
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3633 @INPROCEEDINGS{NIPS8-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3634 editor = {Touretzky, D. S. and Mozer, M. and Hasselmo, M.E.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3635 title = {Advances in Neural Information Processing Systems 8 (NIPS'95)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3636 booktitle = {NIPS 8},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3637 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3638 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3639 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3640
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3641
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3642 @INPROCEEDINGS{NIPS9-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3643 editor = {Mozer, M. and Jordan, M.I. and Petsche, T.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3644 title = {Advances in Neural Information Processing Systems 9 (NIPS'96)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3645 booktitle = {NIPS 9},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3646 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3647 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3648 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3649
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3650
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3651 @INPROCEEDINGS{nnlm:2001:nips,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3652 author = {Bengio, Yoshua and Ducharme, R{\'{e}}jean and Vincent, Pascal},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3653 title = {A Neural Probabilistic Language Model},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3654 year = {2001},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3655 crossref = {NIPS13-shorter},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3656 abstract = {A goal of statistical language modeling is to learn the joint probability function of sequences of words. This is intrinsically difficult because of the curse of dimensionality: we propose to fight it with its own weapons. In the proposed approach one learns simultaneously (1) a distributed representation for each word (i.e. a similarity between words) along with (2) the probability function for word sequences, expressed with these representations. Generalization is obtained because a sequence of words that
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3657 has never been seen before gets high probability if it is made of words that are similar to words forming an already seen sentence. We report on experiments using neural networks for the probability function, showing on two text corpora that the proposed approach very significantly improves on a state-of-the-art trigram model.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3658 topics={Markov,Unsupervised,Language},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3659 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3660
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3661 @INPROCEEDINGS{nsvn:2000:ijcnn,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3662 author = {Vincent, Pascal and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3663 title = {A Neural Support Vector Network Architecture with Adaptive Kernels},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3664 booktitle = {International Joint Conference on Neural Networks 2000},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3665 volume = {V},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3666 year = {2000},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3667 pages = {187--192},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3668 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/nsvn.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3669 abstract = {In the Support Vector Machines ({SVM}) framework, the positive-definite kernel can be seen as representing a fixed similarity measure between two patterns, and a discriminant function is obtained by taking a linear combination of the kernels computed at training examples called support vectors. Here we investigate learning architectures in which the kernel functions can be replaced by more general similarity measures that can have arbitrary internal parameters. The training criterion used in {SVM}s is not appropriate for this purpose so we adopt the simple criterion that is generally used when training neural networks for classification tasks. Several experiments are performed which show that such Neural Support Vector Networks perform similarly to {SVM}s while requiring significantly fewer support vectors, even when the similarity measure has no internal parameters.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3670 topics={Kernel},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3671 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3672
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3673 @INPROCEEDINGS{Ouimet+al-2005,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3674 author = {Ouimet, Marie and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3675 editor = {Cowell, Robert G. and Ghahramani, Zoubin},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3676 title = {Greedy Spectral Embedding},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3677 booktitle = {Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3678 year = {2005},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3679 pages = {253--260},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3680 publisher = {Society for Artificial Intelligence and Statistics},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3681 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/greedy-kernel-aistats05.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3682 abstract = {Spectral dimensionality reduction methods and spectral clustering methods require computation of the principal eigenvectors of an n X n matrix where n is the number of examples. Following up on previously proposed techniques to speed-up kernel methods by focusing on a subset of m examples, we study a greedy selection procedure for this subset, based on the feature space distance between a candidate example and the span of the previously chosen ones. In the case of kernel {PCA} or spectral clustering this reduces computation to O(m^2 n). For the same computational complexity, we can also compute the feature space projection of the non-selected examples on the subspace spanned by the selected examples, to estimate the embedding function based on all the data, which yields considerably better estimation of the embedding function. This algorithm can be formulated in an online setting and we can bound the error on the approximation of the Gram matrix.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3683 topics={HighDimensional,kenel},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3684 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3685
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3686 @MASTERSTHESIS{Ouimet-Msc-2004,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3687 author = {Ouimet, Marie},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3688 keywords = {algorithmes voraces., apprentissage non-supervis{\'{e}}, m{\'{e}}thodes spectrales, noyaux, r{\'{e}}duction de dimensionnalit{\'{e}}},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3689 title = {R{\'{e}}duction de dimensionnalit{\'{e}} non lin{\'{e}}aire et vorace},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3690 year = {2004},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3691 school = {Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3692 abstract = {Les m{\'{e}}thodes spectrales de r{\'{e}}duction de dimensionnalit{\'{e}} et les m{\'{e}}thodes de segmentation spectrale exigent le calcul des vecteurs propres principaux d'une matrice de taille n x n o{\`{u}} n est le nombre d'exemples. Des techniques ont {\'{e}}t{\'{e}} propos{\'{e}}es dans la litt{\'{e}}rature pour acc{\'{e}}l{\'{e}}rer les m{\'{e}}thodes {\`{a}} noyau en se concentrant sur un sous-ensemble de m exemples. Nous proposons une proc{\'{e}}dure vorace pour la s{\'{e}}lection de ce sous-ensemble, qui est bas{\'{e}}e sur la distance dans l'espace des caract{\`{e}}ristiques entre un exemple candidat et le sous-espace g{\'{e}}n{\'{e}}r{\'{e}} par les exemples pr{\'{e}}c{\'{e}}demment choisis. Dans le cas de l'ACP {\`{a}} noyau ou de la segmentation spectrale, nous obtenons un algorithme en O(m*m*n), o{\`{u}} m << n, qui, contrairement aux techniques pr{\'{e}}c{\'{e}}demment propos{\'{e}}es, peut se formuler de fa{\c c}on en-ligne. Pour la m{\^{e}}me complexit{\'{e}} en temps, nous pouvons {\'{e}}galement calculer la projection des exemples non choisis sur le sous-espace engendr{\'{e}} par les exemples choisis dans l'espace des caract{\'{e}}ristiques. En repr{\'{e}}sentant ainsi les exemples par leur projection nous obtenons une approximation de plus faible rang de la matrice de Gram sur toutes les donn{\'{e}}es. Nous pouvons {\'{e}}galement borner l'erreur correspondant {\`{a}} cette approximation de la matrice de Gram.}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3693 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3694
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3695 @ARTICLE{paiement+bengio+eck:aij,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3696 author = {Paiement, Jean-Fran{\c c}ois and Bengio, Samy and Eck, Douglas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3697 title = {Probabilistic Models for Melodic Prediction},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3698 journal = {Artificial Intelligence Journal},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3699 volume = {173},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3700 year = {2009},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3701 pages = {1266-1274},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3702 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3703 sourcetype={Journal},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3704 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3705
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3706 @INPROCEEDINGS{paiement+eck+bengio+barber:icml2005,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3707 author = {Paiement, Jean-Fran{\c c}ois and Eck, Douglas and Bengio, Samy and Barber, D.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3708 title = {A graphical model for chord progressions embedded in a psychoacoustic space},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3709 year = {2005},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3710 pages = {641--648},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3711 publisher = {ACM Press},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3712 crossref = {ICML05},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3713 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3714 sourcetype={Conference},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3715 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3716
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3717 @INPROCEEDINGS{paiement+eck+bengio:ccai2006,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3718 author = {Paiement, Jean-Fran{\c c}ois and Eck, Douglas and Bengio, Samy},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3719 editor = {Lamontagne, Luc and Marchand, Mario},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3720 title = {Probabilistic Melodic Harmonization},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3721 booktitle = {Canadian Conference on AI},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3722 series = {Lecture Notes in Computer Science},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3723 volume = {4013},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3724 year = {2006},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3725 pages = {218-229},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3726 publisher = {Springer},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3727 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3728 sourcetype={Conference},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3729 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3730
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3731 @INPROCEEDINGS{paiement+eck+bengio:ismir2005,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3732 author = {Paiement, Jean-Fran{\c c}ois and Eck, Douglas and Bengio, Samy},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3733 title = {A Probabilistic Model for Chord Progressions},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3734 booktitle = {{Proceedings of the 6th International Conference on Music Information Retrieval ({ISMIR} 2005)}},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3735 year = {2005},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3736 pages = {312-319},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3737 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3738 sourcetype={Conference},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3739 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3740
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3741 @INPROCEEDINGS{paiement+grandvalet+bengio+eck:icml2008,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3742 author = {Paiement, Jean-Fran{\c c}ois and Grandvalet, Yves and Bengio, Samy and Eck, Douglas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3743 title = {A generative model for rhythms},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3744 year = {2008},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3745 pages = {},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3746 crossref = {ICML06-shorter},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3747 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3748 sourcetype={Conference},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3749 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3750
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3751 @UNPUBLISHED{paiement+grandvalet+bengio+eck:nipsworkshop2007,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3752 author = {Paiement, Jean-Fran{\c c}ois and Grandvalet, Yves and Bengio, Samy and Eck, Douglas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3753 title = {A generative model for rhythms},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3754 year = {2007},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3755 note = {NIPS 2007 Workshop on Music, Brain and Cognition},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3756 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3757 sourcetype={Workshop},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3758 optkey={""},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3759 optmonth={""},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3760 optannote={""},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3761 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3762
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3763 @MASTERSTHESIS{Paiement-Msc-2003,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3764 author = {Paiement, Jean-Fran{\c c}ois},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3765 keywords = {algorithmes, apprentissage, apprentissage non supervis{\'{e}}, forage de donn{\'{e}}es, noyaux, r{\'{e}}duction de dimensions, statistique, Statistiques},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3766 title = {G{\'{e}}n{\'{e}}ralisation d'algorithmes de r{\'{e}}duction de dimension},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3767 year = {2003},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3768 school = {Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3769 abstract = {On pr{\'{e}}sente tout d'abord la notion de vari{\'{e}}t{\'{e}} comme r{\'{e}}gion de faible dimension contenant des observations situ{\'{e}}es dans un espace de haute dimension. Cette d{\'{e}}finition justifie l'{\'{e}}laboration d'algorithmes permettant d'exprimer les donn{\'{e}}es dans un syst{\`{e}}me de coordonn{\'{e}}es de dimensions {\'{e}}gale {\`{a}} celle de la vari{\'{e}}t{\'{e}} sur laquelle les donn{\'{e}}es sont approximativement situ{\'{e}}es.
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3770 La notion de noyau comme mesure de similarit{\'{e}} est par la suite formalis{\'{e}}e. On constate que l'application d'un noyau {\`{a}} deux observations correspond {\`{a}} l'{\'{e}}valuation d'un produit scalaire dans un espace de Hilbert appel{\'{e}} espace de caract{\'{e}}ristiques.
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3771 Une m{\'{e}}thode de r{\'{e}}duction de dimension lin{\'{e}}raire est expos{\'{e}}e ainsi que ces limites. Des algorithmes non lin{\'{e}}raires de r{\'{e}}duction de dimension et de segmentation permettent de s'affranchir de ces limites. Ces derniers ne fournissent cependant pas d'extension directe {\`{a}} des points hors {\'{e}}chantillon.
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3772 L'{\'{e}}tape fondamentale au sein des algorithmes pr{\'{e}}sent{\'{e}}s est la solution d'un syst{\`{e}}me de vecteurs propres d'une matrice sym{\'{e}}trique cr{\'{e}}{\'{e}}e {\`{a}} partir d'un noyau d{\'{e}}pendant des donn{\'{e}}es. On con{\c c}oit cd probl{\`{e}}me comme le fait de trouver les fonctions propres d'un op{\'{e}}rateur lin{\'{e}}aire d{\'{e}}fini {\`{a}} partir du m{\^{e}}me noyau. On utilise alors la formulation de Nystr{\"{o}}m, pr{\'{e}}sente dans l'espace en composantes principales {\`{a}} noyaux, afin de r{\'{e}}duire la dimension des points hors {\'{e}}chantillon sur la vase des plongements obtenus {\`{a}} l'aide des algorithmes d{\'{e}}j{\`{a}} mentionn{\'{e}}s.
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3773 La qualit{\'{e}} de la projection g{\'{e}}n{\'{e}}r{\'{e}}e est compar{\'{e}}e {\`{a}} la perturbation intrins{\`{e}}que des algorithmes si on substitue certaine observations par d'autres tir{\'{e}}es de la m{\^{e}}me distribution.}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3774 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3775
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3776 @ARTICLE{perez+gers+schmidhuber+eck:2002,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3777 author = {Perez-Ortiz, J. A. and Gers, F. A. and Eck, Douglas and Schmidhuber, Juergen},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3778 title = {{K}alman filters improve {LSTM} network performance in problems unsolvable by traditional recurrent nets},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3779 journal = {Neural Networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3780 volume = {16},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3781 number = {2},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3782 year = {2003},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3783 abstract = {The Long Short-Term Memory ({LSTM}) network trained by gradient descent solves difficult problems which traditional recurrent neural networks in general cannot. We have recently observed that the decoupled extended Kalman filter training algorithm allows for even better performance, reducing significantly the number of training steps when compared to the original gradient descent training algorithm. In this paper we present a set of experiments which are unsolvable by classical recurrent networks but which are solved elegantly and robustly and quickly by {LSTM} combined with Kalman filters.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3784 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3785 sourcetype={Journal},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3786 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3787
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3788 @ARTICLE{perez+gers+schmidhuber+eck:2003,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3789 author = {Perez-Ortiz, J. A. and Gers, F. A. and Eck, Douglas and Schmidhuber, Juergen},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3790 title = {{K}alman filters improve {LSTM} network performance in problems unsolvable by traditional recurrent nets},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3791 journal = {Neural Networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3792 volume = {16},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3793 number = {2},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3794 year = {2003},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3795 pages = {241--250},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3796 abstract = {The Long Short-Term Memory ({LSTM}) network trained by gradient descent solves difficult problems which traditional recurrent neural networks in general cannot. We have recently observed that the decoupled extended Kalman filter training algorithm allows for even better performance, reducing significantly the number of training steps when compared to the original gradient descent training algorithm. In this paper we present a set of experiments which are unsolvable by classical recurrent networks but which are solved elegantly and robustly and quickly by {LSTM} combined with Kalman filters.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3797 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3798 sourcetype={Journal},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3799 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3800
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3801 @INPROCEEDINGS{perez+schmidhuber+gers+eck:icannB2002,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3802 author = {Perez-Ortiz, J. A. and Schmidhuber, Juergen and Gers, F. A. and Eck, Douglas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3803 editor = {Dorronsoro, J.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3804 title = {Improving Long-Term Online Prediction with {Decoupled Extended Kalman Filters}},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3805 booktitle = {{Artificial Neural Networks -- ICANN 2002 (Proceedings)}},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3806 year = {2002},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3807 pages = {1055--1060},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3808 publisher = {Springer},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3809 abstract = {Long Short-Term Memory ({LSTM}) recurrent neural networks ({RNN}s) outperform traditional {RNN}s when dealing with sequences involving not only short-term but also long-term dependencies. The decoupled extended Kalman filter learning algorithm ({DEKF}) works well in online environments and reduces significantly the number of training steps when compared to the standard gradient-descent algorithms. Previous work on {LSTM}, however, has always used a form of gradient descent and has not focused on true online situations. Here we combine {LSTM} with {DEKF} and show that this new hybrid improves upon the original learning algorithm when applied to online processing.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3810 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3811 sourcetype={Conference},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3812 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3813
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3814 @TECHREPORT{Pigeon-Bengio-96-aH-TR,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3815 author = {Pigeon, Steven and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3816 title = {A Memory-Efficient Huffman Adaptive Coding Algorithm for Very Large Sets of Symbols},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3817 number = {\#1081},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3818 year = {1997},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3819 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3820 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/HuffAdapt.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3821 abstract = {The problem of computing the minimum redundancy codes as we observe symbols one by one has received a lot of attention. However, existing algorithm implicitly assumes that either we have a small alphabet — quite typically 256 symbols — or that we have an arbitrary amount of memory at our disposal for the creation of the tree. In real life applications one may need to encode symbols coming from a much larger alphabet, for e.g. coding integers. We now have to deal not with hundreds of symbols but possibly with millions of symbols. While other algorithms use a space proportional to the number of observed symbol, we here propose one that uses space proportional to the number of frequency classes, which is, quite interestingly, always smaller or equal to the number of observed symbols.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3822 topics={Compression},cat={T},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3823 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3824
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3825 @INPROCEEDINGS{Pigeon-dcc98,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3826 author = {Pigeon, Steven and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3827 editor = {Society, {IEEE} Computer},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3828 title = {A Memory-Efficient Adaptive Huffman Coding Algorithm for Very Large Sets of Symbols},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3829 booktitle = {Data Compression Conference},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3830 year = {1998},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3831 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/dcc98.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3832 abstract = {The problem of computing the minimum redundancy codes as we observe symbols one by one has received a lot of attention. However, existing algorithms implicitly assumes that either we have a small alphabet — quite typically 256 symbols — or that we have an arbitrary amount of memory at our disposal for the creation of the tree. In real life applications one may need to
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3833 encode symbols coming from a much larger alphabet, for e.g. coding integers. We now have to deal not with hundreds of symbols but possibly with millions of symbols. While other algorithms use a space proportional to the number of observed symbols, we here propose one that uses space proportional to the number of frequency classes, which is, quite interestingly, always smaller or equal to the size of the alphabet.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3834 topics={Compression},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3835 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3836
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3837 @INPROCEEDINGS{Pigeon-dcc99,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3838 author = {Pigeon, Steven and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3839 editor = {Society, {IEEE} Computer},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3840 title = {Binary Pseudowavelets and Applications to Bilevel Image Processing},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3841 booktitle = {Data Compression Conference},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3842 year = {1999},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3843 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/dcc99.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3844 abstract = {This paper shows the existance of binary pseudowavelets, bases on the binary domain that exhibit some of the properties of wavelets, such as multiresolution reconstruction and compact support. The binary pseudowavelets are defined on _n (binary vectors of length n) and are operated upon with the binary operators logical and and exclusive or. The forward transform, or analysis, is the decomposition of a binary vector into its constituant binary pseudowavelets. Binary pseudowavelets allow multiresolution, progressive reconstruction of binary vectors by using progressively more coefficients in the inverse transform. Binary pseudowavelets bases, being sparse matrices, also provide for fast transforms; moreover pseudowavelets rely on hardware-friendly operations for efficient software and hardware implementation.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3845 topics={Compression},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3846 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3847
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3848 @TECHREPORT{Pigeon-Huffman-TR98,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3849 author = {Pigeon, Steven and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3850 title = {A Memory-Efficient Adaptive Huffman Coding for Very Large Sets of Symbols revisited},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3851 number = {1095},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3852 year = {1998},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3853 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3854 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/TechRep_AdaptativeHuffman2.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3855 abstract = {While algorithm M (presented in A Memory-Efficient Huffman Adaptive Coding Algorithm for Very Large Sets of Symbols, by Steven Pigeon & Yoshua Bengio, Universit{\'{e}} de Montr{\'{e}}al technical report #1081 [1]) converges to the entropy of the signal, it also assumes that the characteristics of the signal are stationary, that is, that they do not change over time and that successive adjustments, ever decreasing in their magnitude, will lead to a reasonable approximation of the entropy. While this is true for some data, it is clearly not true for some other. We present here a modification of the M algorithm that allows negative updates. Negative updates are used to maintain a window over the source. Symbols enter the window at its right and will leave it at its left, after w steps (the window width). The algorithm presented here allows us to update correctly the weights of the symbols in the symbol tree. Here, we will also have negative migration or demotion, while we only had positive migration or promotion in M. This algorithm will be called M+.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3856 topics={Compression},cat={T},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3857 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3858
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3859 @PHDTHESIS{Pigeon-Phd-2001,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3860 author = {Pigeon, Steven},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3861 keywords = {algorithmes, codes adaptatifs, codes de Golomb, codes universels, Compression de donn{\'{e}}es, compression LZ78, LZW, ondelettes, pseudo-ondelettes},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3862 title = {Contributions {\`{a}} la compression de donn{\'{e}}es},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3863 year = {2001},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3864 school = {Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3865 abstract = {L'objectif de cette th{\`{e}}se est de pr{\'{e}}senter nos contributions {\`{a}} la compression de donn{\'{e}}es. Le texte entier n'est pas consacr{\'{e}} {\`{a}} nos seules contributions. Une large part est consacr{\'{e}}e au mat{\'{e}}riel introductif et {\`{a}} la recension de litt{\'{e}}rature sur les sujets qui sont pertinents {\`{a}} nos contributions. Le premier chapitre de contribution, le chapitre "Contribution au codage des entiers" se concentre sur le probl{\`{e}}me de la g{\'{e}}n{\'{e}}ration de codes efficaces pour les entiers. Le chapitre "Codage Huffman Adaptatif" pr{\'{e}}sente deux nouveaux algorithmes pour la g{\'{e}}n{\'{e}}ration dynamique de codes structur{\'{e}}s en arbre, c'est-{\`{a}}-dire des codes de type Huffman. Le chapitre "LZW avec une perte" explore le probl{\`{e}}me de la compression d'images comportant un petit nombre de couleurs distinctes et propose une extension avec perte d'un algorithme originalement sans perte, LZW. Enfin, le dernier chapitre de contribution, le chapitre "Les pseudo-ondelettes binaires" pr{\'{e}}sente une solution original au probl{\`{e}}me de l'analyse multir{\'{e}}solution des images monochromes, c'est-{\`{a}}-dire des images n'ayant que deux couleurs, conventionnellement noir et blanc. Ce type d'image correspond par exemple aux images textuelles telle que produites par un processus de transmission de type facsimil{\'{e}}.}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3866 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3867
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3868 @ARTICLE{Pigeon98,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3869 author = {Pigeon, Steven and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3870 title = {Memory-Efficient Adaptive Huffman Coding},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3871 journal = {Dr. Dobb's Journal},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3872 volume = {290},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3873 year = {1998},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3874 pages = {131--135},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3875 topics={Compression},cat={J},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3876 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3877
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3878 @INPROCEEDINGS{probnn:2000:ijcnn,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3879 author = {Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3880 title = {Probabilistic Neural Network Models for Sequential Data},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3881 booktitle = {International Joint Conference on Neural Networks 2000},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3882 volume = {V},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3883 year = {2000},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3884 pages = {79--84},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3885 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/81_01.PDF},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3886 abstract = {It has already been shown how Artificial Neural Networks ({ANN}s) can be incorporated into probabilistic models.
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3887 In this paper we review some of the approaches which have been proposed to incorporate them into probabilistic
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3888 models of sequential data, such as Hidden {Markov} Models ({HMM}s). We also discuss new developments and new
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3889 ideas in this area, in particular how {ANN}s can be used to model high-dimensional discrete and continuous data to
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3890 deal with the curse of dimensionality, and how the ideas proposed in these models could be applied to statistical
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3891 language modeling to represent longer-term context than allowed by trigram models, while keeping word-order
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3892 information.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3893 topics={Markov},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3894 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3895
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3896 @UNPUBLISHED{pugin+burgoyne+eck+fujinaga:nipsworkshop2007,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3897 author = {Pugin, L. and Burgoyne, J. A. and Eck, Douglas and Fujinaga, I.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3898 title = {Book-adaptive and book-dependant models to accelerate digitalization of early music},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3899 year = {2007},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3900 note = {NIPS 2007 Workshop on Music, Brain and Cognition},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3901 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3902 sourcetype={Workshop},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3903 optkey={""},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3904 optmonth={""},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3905 optannote={""},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3906 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3907
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3908 @INPROCEEDINGS{Rahim-97,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3909 author = {Rahim, Mazin and Bengio, Yoshua and {LeCun}, Yann},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3910 title = {Discriminative feature and model design for automatic speech recognition},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3911 booktitle = {Proceedings of Eurospeech 1997},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3912 year = {1997},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3913 pages = {75--78},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3914 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/rahim-bengio-lecun-97.ps.gz},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3915 abstract = {A system for discriminative feature and model design is presented for automatic speech recognition. Training based on minimum classification error with a single objective function is applied for designing a set of parallel networks performing feature transformation and a set of hidden {Markov} models performing speech recognition. This paper compares the use of linear and non-linear functional transformations when applied to conventional recognition features, such as spectrum or cepstrum. It also provides a framework for integrated feature and model training when using class-specific transformations. Experimental results on telephone-based connected digit recognition are presented.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3916 topics={Speech},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3917 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3918
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3919 @ARTICLE{Rivest-2009,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3920 author = {Rivest, Fran{\c c}ois and Kalaska, John and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3921 title = {Alternative Time Representations in Dopamine Models},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3922 journal = {Journal of Computational Neuroscience},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3923 volume = {28},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3924 number = {1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3925 year = {2009},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3926 pages = {107--130},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3927 abstract = {Dopaminergic neuron activity has been modeled during learning and appetitive behavior, most commonly using the temporal-difference (TD) algorithm. However, a proper representation of elapsed time and of the exact task is usually required for the model to work. Most models use timing elements such as delay-line representations of time that are not biologically realistic for intervals in the range of seconds. The interval-timing literature provides several alternatives. One of them is that timing could emerge from general network dynamics, instead of coming from a dedicated circuit. Here, we present a general rate-based learning model based on long short-term memory ({LSTM}) networks that learns a time representation when needed. Using a na{\"{\i}}ve network learning its environment in conjunction with TD, we reproduce dopamine activity in appetitive trace conditioning with a constant CS-US interval, including probe trials with unexpected delays. The proposed model learns a representation of the environment dynamics in an adaptive biologically plausible framework, without recourse to delay lines or other special-purpose circuits. Instead, the model predicts that the task-dependent representation of time is learned by experience, is encoded in ramp-like changes in single-neuron activity distributed across small neural networks, and reflects a temporal integration mechanism resulting from the inherent dynamics of recurrent loops within the network. The model also reproduces the known finding that trace conditioning is more difficult than delay conditioning and that the learned representation of the task can be highly dependent on the types of trials experienced during training. Finally, it suggests that the phasic dopaminergic signal could facilitate learning in the cortex.}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3928 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3929
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3930 @ARTICLE{schmidhuber+gers+eck:2002,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3931 author = {Schmidhuber, Juergen and Gers, F. A. and Eck, Douglas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3932 title = {Learning Nonregular Languages: A Comparison of Simple Recurrent Networks and {LSTM}},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3933 journal = {Neural Computation},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3934 volume = {14},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3935 number = {9},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3936 year = {2002},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3937 pages = {2039--2041},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3938 abstract = {In response to Rodriguez' recent article (Rodriguez 2001) we compare the performance of simple recurrent nets and {\em ``Long Short-Term Memory''} ({LSTM}) recurrent nets on context-free and context-sensitive languages.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3939 source={OwnPublication},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3940 sourcetype={Journal},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3941 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3942
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3943 @TECHREPORT{Schwenk-Bengio-97-TR,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3944 author = {Schwenk, Holger and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3945 title = {Adaptive Boosting of Neural Networks for Character Recognition},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3946 number = {\#1072},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3947 year = {1997},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3948 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3949 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/AdaBoostTR.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3950 abstract = {”Boosting” is a general method for improving the performance of any learning algorithm that consistently generates classifiers which need to perform only slightly better than random guessing. A recently proposed and very promising boosting algorithm is AdaBoost [5]. It has been applied with great success to several benchmark machine learning problems using rather simple learning algorithms [4], in particular decision trees [1, 2, 6]. In this paper we use AdaBoost to improve the performances of neural networks applied to character recognition tasks. We compare training methods based on sampling the training set and weighting the cost function. Our system achieves about 1.4\% error on a data base of online handwritten digits from more than 200 writers. Adaptive boosting of a multi-layer network achieved 2\% error on the UCI Letters offline characters data set.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3951 topics={Boosting,Speech},cat={T},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3952 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3953
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3954 @INPROCEEDINGS{Schwenk-nips10,
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fsavard
parents:
diff changeset
3955 author = {Schwenk, Holger and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3956 title = {Training Methods for Adaptive Boosting of Neural Networks for Character Recognition},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3957 year = {1998},
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fsavard
parents:
diff changeset
3958 crossref = {NIPS10-shorter},
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fsavard
parents:
diff changeset
3959 abstract = {”Boosting” is a general method for improving the performance of any learning algorithm that consistently generates classifiers which need to perform only slightly better than random guessing. A recently proposed and very promising boosting algorithm is AdaBoost [5]. It has been applied with great success to several benchmark machine learning problems using rather simple learning algorithms [4], in particular decision trees [1, 2, 6]. In this paper we use AdaBoost to improve the performances of neural networks applied to character recognition tasks. We compare training methods based on sampling the training set and weighting the cost function. Our system achieves about 1.4\% error on a data base of online handwritten digits from more than 200 writers. Adaptive boosting of a multi-layer network achieved 2\% error on the UCI Letters offline characters data set.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3960 topics={Boosting,Speech},cat={C},
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fsavard
parents:
diff changeset
3961 }
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fsavard
parents:
diff changeset
3962
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3963 @ARTICLE{Schwenk2000,
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fsavard
parents:
diff changeset
3964 author = {Schwenk, Holger and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3965 title = {Boosting Neural Networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3966 journal = {Neural Computation},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3967 volume = {12},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3968 number = {8},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3969 year = {2000},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3970 pages = {1869--1887},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3971 abstract = {“Boosting” is a general method for improving the performance of learning algorithms. A recently proposed boosting algorithm is AdaBoost. It has been applied with great success to several benchmark machine learning problems using mainly decision trees as base classifiers. In this paper we investigate whether AdaBoost also works as well with neural networks, and we discuss the advantages and drawbacks of di_erent versions of the AdaBoost algorithm. In particular, we compare training methods based on sampling the training set and weighting the cost function. The results suggest that random resampling of the training data is not the main explanation of the success of the improvements brought by AdaBoost. This is in contrast to Bagging which directly aims at reducing variance and for which random resampling is essential to obtain the reduction in generalization error. Our system achieves about 1.4\% error on a data set of online handwritten digits from more than 200 writers. A boosted multi-layer network achieved 1.5\% error on the UCI Letters and 8.1\% error on the UCI satellite data set, which is significantly better than boosted decision trees.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3972 topics={Boosting},cat={J},
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fsavard
parents:
diff changeset
3973 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3974
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3975 @INPROCEEDINGS{secondorder:2001:nips,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3976 author = {Dugas, Charles and Bengio, Yoshua and Belisle, Francois and Nadeau, Claude and Garcia, Rene},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3977 title = {Incorporating Second-Order Functional Knowledge for Better Option Pricing},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3978 year = {2001},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3979 crossref = {NIPS13-shorter},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3980 abstract = {Incorporating prior knowledge of a particular task into the architecture of a learning algorithm can greatly improve generalization performance. We study here a case where we know that the function to be learned is non-decreasing in two of its arguments and convex in one of them. For this purpose we propose a class of functions similar to multi-layer neural networks but (1) that has those properties, (2) is a universal approximator of continuous functions with these and other properties. We apply this new class of functions to the task of modeling the price of call options. Experiments show improvements on regressing the price of call options using the new types of function classes that incorporate the a priori constraints.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3981 topics={Finance},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3982 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3983
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3984 @ARTICLE{Sonnenburg+al-2007,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3985 author = {Sonnenburg, Soeren and et al. and Vincent, Pascal},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3986 title = {The Need for Open Source Software in Machine Learning.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3987 year = {2007},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3988 note = {institution: Fraunhofer Publica [http://publica.fraunhofer.de/oai.har] (Germany)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3989 journal = {Journal of Machine Learning Research},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3990 abstract = {all authors: Sonnenburg, S. and Braun, M.L. and Ong, C.S. and Bengio, S. and Bottou, L. and Holmes, G. and {LeCun}, Y. and M{\~{A}}¼ller, K.-R. and Pereira, F. and Rasmussen, C.E. and R{\~{A}}¤tsch, G. and Sch{\~{A}}{\P}lkopf, B. and Smola, A. and Vincent, P. and Weston, J. and Williamson, R.C.
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3991
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3992 Open source tools have recently reached a level of maturity which makes them suitable for building large-scale real-world systems. At the same time, the field of machine learning has developed a large body of powerful learning algorithms for diverse applications. However, the true potential of these methods is not used, since existing implementations are not openly shared, resulting in software with low usability, and weak interoperability. We argue that this situation can be significantly improved by increasing incentives for researchers to publish their software under an open source model. Additionally, we outline the problems authors are faced with when trying to publish algorithmic implementations of machine learning methods. We believe that a resource of peer reviewed software accompanied by short articles would be highly valuable to both the machine learning and the general scientific community.}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3993 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3994
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3995 @ARTICLE{Takeuchi-Bengio-Kanamori-2002,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3996 author = {Takeuchi, Ichiro and Bengio, Yoshua and Kanamori, Takafumi},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3997 title = {Robust Regression with Asymmetric Heavy-Tail Noise Distributions},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3998 journal = {Neural Computation},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
3999 volume = {14},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4000 number = {10},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4001 year = {2002},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4002 pages = {2469--2496},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4003 abstract = {In the presence of a heavy-tail noise distribution, regression becomes much more difficult. Traditional robust regression methods assume that the noise distribution is symmetric and they down-weight the influence of so-called outliers. When the noise distribution is assymetric these methods yield biased regression estimators. Motivated by data-mining problems for the insurance industry, we propose in this paper a new approach to robust regession that is tailored to deal with the case where the noise distribution is asymmetric. The main idea is to learn most of the parameters of the model using conditional quantile estimators (which are biased but robust etimators of the regression), and to lern a few remaining parameters to combbine and correct these stimators, to unbiasedly minimize the average squared error. Theoritical analysis and experiments show the clear advantages of the approach. Results are on artificial data as well as real insurance data, using both linear and neural-network predictors.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4004 topics={Mining},cat={J},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4005 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4006
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4007 @ARTICLE{Thierry+al-2008,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4008 author = {Bertin-Mahieux, Thierry and Eck, Douglas and Maillet, Fran{\c c}ois and Lamere, Paul},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4009 title = {Autotagger: A Model For Predicting Social Tags from Acoustic Features on Large Music Databases},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4010 journal = {Journal of New Music Research},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4011 year = {2008},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4012 abstract = {Social tags are user-generated keywords associated with some resource on the Web. In the case of music, social tags have become an important component of "Web 2.0" recommender systems, allowing users to generate playlists based on use-dependent terms such as chill or jogging that have been applied to particular songs. In this paper, we propose a method for predicting these social tags directly from MP3 files. Using a set of 360 classifiers trained using the online ensemble learning algorithm FilterBoost, we map audio features onto social tags collected from the Web. The resulting automatic tags (or autotags) furnish information about music that is otherwise untagged or poorly tagged, allowing for insertion of previously unheard music into a social recommender. This avoids the “cold-start problem” common in such systems. Autotags can also be used to smooth the tag space from which similarities and
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4013 recommendations are made by providing a set of comparable baseline tags for all tracks in a recommender system. Because the words we learn are the same as those used by people who label their music collections, it is easy to integrate our predictions into existing similarity and prediction methods based on web data.}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4014 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4015
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4016 @ARTICLE{Thivierge+al-2007,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4017 author = {Thivierge, J. -P. and Rivest, Fran{\c c}ois and Monchi, O},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4018 title = {Spiking Neurons, Dopamine, and Plasticity: Timing Is Everything, But Concentration Also Matters},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4019 journal = {Synapse},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4020 volume = {61},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4021 year = {2007},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4022 pages = {375-390},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4023 abstract = {While both dopamine (DA) fluctuations and spike-timing-dependent plasticity (STDP) are known to influence long-term corticostriatal plasticity, little attention has been devoted to the interaction between these two fundamental mechanisms. Here, a theoretical framework is proposed to account for experimental results specifying the role of presynaptic activation, postsynaptic activation, and concentrations of extracellular DA in synaptic plasticity. Our starting point was an explicitly-implemented multiplicative rule linking STDP to Michaelis-Menton equations that models the dynamics of extracellular DA fluctuations. This rule captures a wide range of results on conditions leading to long-term potentiation and depression in simulations that manipulate the frequency of induced corticostriatal stimulation and DA release. A well-documented biphasic function relating DA concentrations to synaptic plasticity emerges naturally from simulations involving a multiplicative rule linking DA and neural activity. This biphasic function is found consistently across different neural coding schemes employed (voltage-based vs. spike-based models). By comparison, an additive rule fails to capture these results. The proposed framework is the first to generate testable predictions on the dual influence of DA concentrations and STDP on long-term plasticity, suggesting a way in which the biphasic influence of DA concentrations can modulate the direction and magnitude of change induced by STDP, and raising the possibility that DA concentrations may inverse the LTP/LTD components of the STDP rule.}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4024 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4025
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4026 @TECHREPORT{tonga-tr,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4027 author = {Le Roux, Nicolas and Manzagol, Pierre-Antoine and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4028 title = {Topmoumoute online natural gradient algorithm},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4029 number = {1299},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4030 year = {2007},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4031 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4032 abstract = {Guided by the goal of obtaining an optimization algorithm that is
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4033 both fast and yielding good generalization, we study the descent direction maximizing the decrease in generalization error or the probability of not increasing generalization error. The surprising result is that from both the Bayesian and frequentist perspectives this can yield the natural gradient direction. Although that direction can be very expensive to compute we develop an efficient, general, online approximation to the natural gradient descent which is suited to large scale problems. We report experimental results showing much faster convergence in computation time and in number of iterations with TONGA (Topmoumoute Online natural Gradient Algorithm) than with stochastic gradient descent, even on very large datasets.}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4034 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4035
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4036 @TECHREPORT{TR1197,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4037 author = {Vincent, Pascal and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4038 title = {K-Local Hyperplane and Convex Distance Nearest Neighbor Algorithms},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4039 number = {1197},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4040 year = {2001},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4041 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4042 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/TR1197.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4043 abstract = {Guided by an initial idea of building a complex (non linear) decision surface with maximal local margin in input space, we give a possible geometrical intuition as to why K-Nearest Neighbor ({KNN}) algorithms often perform more poorly than {SVM}s on classification tasks. We then propose modified K-Nearest Neighbor algorithms to overcome the perceived problem. The approach is similar in spirit to Tangent Distance, but with invariances inferred from the local neighborhood rather than prior knowledge. Experimental results on real world classification tasks suggest that the modified {KNN} algorithms often give a dramatic improvement over standard {KNN} and perform as well or better than {SVM}s.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4044 topics={Kernel},cat={T},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4045 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4046
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4047 @TECHREPORT{TR1198,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4048 author = {Takeuchi, Ichiro and Bengio, Yoshua and Kanamori, Takafumi},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4049 title = {Robust Regression with Asymmetric Heavy-Tail Noise},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4050 number = {1198},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4051 year = {2001},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4052 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4053 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/TR1198.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4054 abstract = {In the presence of a heavy-tail noise distribution, regression becomes much more difficult. Traditional robust regression methods assume that the noise distribution is symmetric and they downweight the influence of so-called outliers. When the noise distribution is asymmetric these methods yield strongly biased regression estimators. Motivated by data-mining problems for the insurance industry, we propose in this paper a new approach to robust regression that is tailored to deal with the case where the noise distribution is asymmetric. The main idea is to learn most of the parameters of the model using conditional quantile estimators (which are biased but robust estimators of the regression), and to learn a few remaining parameters to combine and correct these estimators, to minimize the average squared error. Theoretical analysis and experiments show the clear advantages of the approach. Results are on artificial data as well as real insurance data, using both linear and neural-network predictors.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4055 topics={Mining},cat={T},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4056 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4057
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4058 @TECHREPORT{TR1199,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4059 author = {Chapados, Nicolas and Bengio, Yoshua and Vincent, Pascal and Ghosn, Joumana and Dugas, Charles and Takeuchi, Ichiro and Meng, Linyan},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4060 title = {Estimating Car Insurance Premia: a Case Study in High-Dimensional Data Inference},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4061 number = {1199},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4062 year = {2001},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4063 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4064 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/TR1199.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4065 abstract = {Estimating insurance premia from data is a difficult regression problem for several reasons: the large number of variables, many of which are discrete, and the very peculiar shape of the noise distribution, asymmetric with fat tails, with a large majority zeros and a few unreliable and very large values. We introduce a methodology for estimating insurance premia that has been applied in the car insurance industry. It is based on mixtures of specialized neural networks, in order to reduce the effect of outliers on the estimation. Statistical comparisons with several different alternatives, including decision trees and generalized linear models show that the proposed method is significantly more precise, allowing to identify the least and most risky contracts, and reducing the median premium by charging more to the most risky customers.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4066 topics={HighDimensional,Mining},cat={T},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4067 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4068
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4069 @TECHREPORT{TR1200,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4070 author = {Bengio, Yoshua and Chapados, Nicolas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4071 title = {Extending Metric-Based Model Selection and Regularization in the Absence of Unlabeled Data},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4072 number = {1200},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4073 year = {2001},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4074 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4075 url = {http://www.iro.umontreal.ca/lisa/pointeurs/TR1200.ps},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4076 abstract = {Metric-based methods have recently been introduced for model selection and regularization, often yielding very significant improvements over all the alternatives tried (including cross-validation). However, these methods require a large set of unlabeled data, which is not always available in many applications. In this paper we extend these methods (TRI, ADJ and ADA) to the case where no unlabeled data is available. The extended methods (xTRI, xADJ, xADA) use a model of the input density directly estimated from the training set. The intuition is that the main reason why the above methods work well is that they make sure that the learned function behaves similarly on the training points and on “neighboring” points. The experiments are based on estimating a simple non-parametric density model. They show that the extended methods perform comparably to the originals even though no unlabeled data is used.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4077 topics={ModelSelection,Finance},cat={T},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4078 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4079
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4080 @TECHREPORT{TR1215,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4081 author = {Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4082 title = {New Distributed Probabilistic Language Models},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4083 number = {1215},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4084 year = {2002},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4085 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4086 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/TR1215.ps},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4087 abstract = {Our previous work on statistical modeling introduced the use of probabilistic feedforward neural networks with shared parameters in order to help dealing with the curse of dimensionality. This work started with the motivation to speed up the above model and to take advantage of prior knowledge e.g., in WordNet or in syntactically labeled data sets, and to better deal with polysemy. With the objective of reaching these goals, we present here a series of new statistical language models, most of which are yet untested.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4088 topics={Markov,Language,Unsupervised},cat={T},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4089 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4090
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4091 @TECHREPORT{TR1216,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4092 author = {Bengio, Yoshua and S{\'{e}}n{\'{e}}cal, Jean-S{\'{e}}bastien},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4093 title = {Quick Training of Probabilistic Neural Nets by Importance Sampling},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4094 number = {1216},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4095 year = {2002},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4096 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4097 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/tr1216.ps},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4098 abstract = {Our previous work on statistical language modeling introduced the use of probabilistic feedforward neural networks to help dealing with the curse of dimensionality. Training this model by maximum likelihood however requires for each example to perform as many network passes as there are words in the vocabulary. Inspired by the contrastive divergence model, we proposed and evaluate sampling-based methods which require network passes only for the observed “positive example” and a few sampled negative example words. A very significant speed-up is obtained with an adaptive importance sampling.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4099 topics={Markov,Language,Unsupervised},cat={T},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4100 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4101
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4102 @TECHREPORT{TR1231,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4103 author = {Bengio, Yoshua and Kermorvant, Christopher},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4104 title = {Extracting Hidden Sense Probabilities from Bitexts},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4105 number = {1231},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4106 year = {2003},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4107 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4108 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/TR1231.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4109 abstract = {We propose a probabilistic model that is inspired by Diab & Resnik’s algorithm to extract disambiguation information from aligned bilingual texts. Like Diab & Resnik’s, the proposed model uses WordNet and the fact that word ambiguities are not always the same in the two languages. The generative model introduces a dependency between two translated words through a common ancestor inWordNet’s ontology. Unlike Diab & Resnik’s algorithm it does not suppose that the translation in the source language has a single meaning.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4110 topics={Language},cat={T},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4111 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4112
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4113 @TECHREPORT{TR1232,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4114 author = {Bengio, Yoshua and Vincent, Pascal and Paiement, Jean-Fran{\c c}ois},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4115 title = {Learning Eigenfunctions of Similarity: Linking Spectral Clustering and Kernel {PCA}},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4116 number = {1232},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4117 year = {2003},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4118 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4119 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/TR1232.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4120 abstract = {In this paper, we show a direct equivalence between spectral clustering and kernel {PCA}, and how both are special cases of a more general learning problem, that of learning the principal eigenfunctions of a kernel, when the functions are from a Hilbert space whose inner product is defined with respect to a density model. This suggests a new approach to unsupervised learning in which abstractions (such as manifolds and clusters) that represent the main features of the data density are extracted. Abstractions discovered at one level can be used to build higher-level abstractions. This paper also discusses how these abstractions can be used to recover a quantitative model of the input density, which is at least useful for evaluative and comparative purposes.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4121 topics={HighDimensional,Kernel,Unsupervised},cat={T},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4122 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4123
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4124 @TECHREPORT{TR1234,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4125 author = {Bengio, Yoshua and Grandvalet, Yves},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4126 title = {No Unbiased Estimator of the Variance of K-Fold Cros-Validation},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4127 number = {1234},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4128 year = {2003},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4129 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4130 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/TR1234.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4131 abstract = {Most machine learning researchers perform quantitative experiments to estimate generalization error and compare the performance of different algorithms (in particular, their proposed algorithm). In order to be able to draw statistically convincing conclusions, it is important for them to also estimate the uncertainty around the error (or error difference) estimate. This paper studies the very commonly used K-fold cross-validation estimator of generalization performance. The main theorem shows that there exists no universal (valid under all distributions) unbiased estimator of the variance of K-fold cross-validation. The analysis that accompanies this result is based on the eigen-decomposition of the covariance matrix of errors, which has only three different eigenvalues corresponding to three degrees of freedom of the matrix and three components of the total variance. This analysis helps to better understand the nature of the problem and how it can make na{\"{\i}}ve estimators (that don’t take into account the error correlations due to the overlap between training and test sets) grossly underestimate variance. This is confirmed by numerical experiments in which the three components of the variance are compared when the difficulty of the learning problem and the number of folds are varied.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4132 topics={Comparative},cat={T},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4133 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4134
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4135 @TECHREPORT{tr1238,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4136 author = {Bengio, Yoshua and Paiement, Jean-Fran{\c c}ois and Vincent, Pascal},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4137 title = {Out-of-Sample Extensions for {LLE}, {I}somap, {MDS}, {E}igenmaps, and Spectral Clustering},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4138 number = {1238},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4139 year = {2003},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4140 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4141 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/tr1238.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4142 abstract = {Several unsupervised learning algorithms based on an eigendecomposition provide either an embedding or a clustering only for given training points, with no straightforward extension for out-of-sample examples short of recomputing eigenvectors. This paper provides algorithms for such an extension for Local Linear Embedding ({LLE}), Isomap, Laplacian Eigenmaps, Multi-Dimensional Scaling (all algorithms which provide lower-dimensional embedding for dimensionality reduction) as well as for Spectral Clustering (which performs non-Gaussian clustering). These extensions stem from a unified framework in which these algorithms are seen as learning eigenfunctions of a kernel. {LLE} and Isomap pose special challenges as the kernel is training-data dependent. Numerical experiments on real data show that the generalizations performed have a level of error comparable to the variability of the embedding algorithms to the choice of training data.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4143 topics={HighDimensional,Kernel,Unsupervised},cat={T},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4144 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4145
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4146 @TECHREPORT{tr1239,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4147 author = {Bengio, Yoshua and Vincent, Pascal and Paiement, Jean-Fran{\c c}ois and Delalleau, Olivier and Ouimet, Marie and Le Roux, Nicolas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4148 title = {Spectral Clustering and Kernel {PCA} are Learning Eigenfunctions},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4149 number = {1239},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4150 year = {2003},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4151 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4152 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/tr1239.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4153 abstract = {In this paper, we show a direct equivalence between spectral clustering and kernel {PCA}, and how both are special cases of a more general learning problem, that of learning the principal eigenfunctions of a kernel, when the functions are from a function space whose scalar product is defined with respect to a density model. This defines a natural mapping for new data points, for methods that only provided an embedding, such as spectral clustering and Laplacian eigenmaps. The analysis hinges on a notion of generalization for embedding algorithms based on the estimation of underlying eigenfunctions, and suggests ways to improve this generalization by smoothing the data empirical distribution.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4154 topics={HighDimensional,Kernel,Unsupervised},cat={T},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4155 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4156
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4157 @TECHREPORT{tr1240,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4158 author = {Vincent, Pascal and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4159 title = {Locally Weighted Full Covariance Gaussian Density Estimation},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4160 number = {1240},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4161 year = {2003},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4162 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4163 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/tr1240.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4164 abstract = {We describe an interesting application of the principle of local learning to density estimation. Locally weighted fitting of a Gaussian with a regularized full covariance matrix yields a density estimator which displays improved behavior in the case where much of the probability mass is concentrated along a low dimensional manifold. While the proposed estimator is not guaranteed to integrate to 1 with a finite sample size, we prove asymptotic convergence to the true density. Experimental results illustrating the advantages of this estimator over classic non-parametric estimators are presented.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4165 topics={HighDimensional,Kernel,Unsupervised},cat={T},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4166 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4167
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4168 @TECHREPORT{tr1247,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4169 author = {Bengio, Yoshua and Delalleau, Olivier and Le Roux, Nicolas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4170 title = {Efficient Non-Parametric Function Induction in Semi-Supervised Learning},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4171 number = {1247},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4172 year = {2004},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4173 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4174 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/tr1247.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4175 abstract = {There has been an increase of interest for semi-supervised learning recently, because of the many datasets with large amounts of unlabeled examples and only a few labeled ones. This paper follows up on proposed non-parametric algorithms which provide an estimated continuous label for the given unlabeled examples. It extends them to function induction algorithms that correspond to the minimization of a regularization criterion applied to an out-of-sample example, and happens to have the form of a Parzen windows regressor. The advantage of the extension is that it allows predicting the label for a new example without having to solve again a linear system of dimension n (the number of unlabeled and labeled training examples), which can cost O(n^3). Experiments show that the extension works well, in the sense of predicting a label close to the one that would have been obtained if the test example had been included in the unlabeled set. This relatively efficient function induction procedure can also be used when n is large to approximate the solution by writing it only in terms of a kernel expansion with m << n terms, and reducing the linear system to m equations in m unknowns.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4176 topics={Kernel,Unsupervised},cat={T},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4177 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4178
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4179 @TECHREPORT{tr1250,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4180 author = {Bengio, Yoshua and Monperrus, Martin},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4181 title = {Discovering shared structure in manifold learning},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4182 number = {1250},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4183 year = {2004},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4184 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4185 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/tr-tangent.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4186 abstract = {We claim and present arguments to the effect that a large class of manifold learning algorithms that are essentially local will suffer from at least four generic problems associated with (1) noise in the data, (2) curvature of the manifold, (3) dimensionality of the manifold, and (4) the presence of many manifolds with little data per manifold. This analysis suggests non-local manifold learning algorithms which attempt to discover shared structure in the tangent planes at different positions. A criterion for such an algorithm is proposed and experiments estimating a tangent plane prediction function are presented. The function has parameters that are shared across space rather than estimated based on the local neighborhood, as in current non-parametric manifold learning algorithms. The results show clearly the advantages of this approach with respect to local manifold learning algorithms.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4187 topics={HighDimensional,Kernel,Unsupervised},cat={T},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4188 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4189
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4190 @TECHREPORT{tr1252,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4191 author = {Bengio, Yoshua and Larochelle, Hugo},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4192 title = {Implantation et analyse d'un mod{\`{e}}le graphique {\`{a}} entra{\^{\i}}nement supervis{\'{e}}, semi-supervis{\'{e}} et non-supervis{\'{e}} pour la d{\'{e}}sambigu{\"{\i}}sation s{\'{e}}mantique},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4193 number = {1252},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4194 year = {2004},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4195 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4196 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/tr1252.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4197 abstract = {La d{\'{e}}sambigu{\"{\i}}sation s{\'{e}}mantique est un sujet qui suscite beaucoup d’int{\'{e}}r{\^{e}}t dans la communaut{\'{e}} scientifique en apprentissage automatique. Quoique cette t{\^{a}}che ait {\'{e}}t{\'{e}} abord{\'{e}}e depuis les d{\'{e}}buts du traitement automatique de la langue, peu de progr{\`{e}}s ont {\'{e}}t{\'{e}} accomplis jusqu’{\`{a}} maintenant. Nous pr{\'{e}}sentons ici une application de d{\'{e}}sambigu{\"{\i}}sation bas{\'{e}}e sur un mod{\`{e}}le graphique probabiliste. Ce mod{\`{e}}le a {\'{e}}t{\'{e}} appris sur des donn{\'{e}}es {\'{e}}tiquet{\'{e}}es, non-{\'{e}}tiquet{\'{e}}es, et sur la hi{\'{e}}rarchie WordNet. Avec peu d’examples d’apprentissage, ses performances sont comparables {\`{a}} celles de l’algorithme de Bayes na{\"{\i}}f. Il pourrait {\'{e}}ventuellement {\^{e}}tre adapt{\'{e}} {\`{a}} des corpus bi-textes.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4198 topics={Unsupervised,Language},cat={T},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4199 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4200
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4201 @TECHREPORT{tr1281,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4202 author = {Le Roux, Nicolas and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4203 title = {Continuous Neural Networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4204 number = {1281},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4205 year = {2006},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4206 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4207 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/continuous_nnet_tr1281.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4208 abstract = {This article extends neural networks to the case of an uncountable number of hidden units, in several
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4209 ways. In the first approach proposed, a finite parametrization is possible, allowing gradient-based
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4210 learning. While having the same number of parameters as an ordinary neural network, its internal
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4211 structure suggests that it can represent some smooth functions much more compactly. Under mild
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4212 assumptions, we also find better error bounds than with ordinary neural networks. Furthermore, this
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4213 parametrization may help reducing the problem of saturation of the neurons. In a second approach, the
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4214 input-to-hidden weights are fully non-parametric, yielding a kernel machine for which we demonstrate
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4215 a simple kernel formula. Interestingly, the resulting kernel machine can be made hyperparameter-free
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4216 and still generalizes in spite of an absence of explicit regularization.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4217 cat={T},topics={Kernel,HighDimensional},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4218 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4219
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4220 @TECHREPORT{tr1282,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4221 author = {Bengio, Yoshua and Lamblin, Pascal and Popovici, Dan and Larochelle, Hugo},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4222 title = {Greedy Layer-Wise Training of Deep Networks},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4223 number = {1282},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4224 year = {2006},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4225 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4226 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/dbn_supervised_tr1282.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4227 abstract = {Deep multi-layer neural networks have many levels of non-linearities, which allows them to potentially
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4228 represent very compactly highly non-linear and highly-varying functions. However, until recently it
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4229 was not clear how to train such deep networks, since gradient-based optimization starting from random
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4230 initialization appears to often get stuck in poor solutions. Hinton et al. recently introduced a greedy
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4231 layer-wise unsupervised learning algorithm for Deep Belief Networks (DBN), a generative model with
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4232 many layers of hidden causal variables. In the context of the above optimization problem, we study
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4233 this algorithm empirically and explore variants to better understand its success and extend it to cases
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4234 where the inputs are continuous or where the structure of the input distribution is not revealing enough
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4235 about the variable to be predicted in a supervised task.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4236 cat={T},topics={HighDimensional,Unsupervised},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4237 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4238
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4239 @TECHREPORT{tr1283,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4240 author = {Carreau, Julie and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4241 title = {A Hybrid {Pareto} Model for Asymmetric Fat-Tail Data},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4242 number = {1283},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4243 year = {2006},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4244 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4245 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/fat_tails_tr1283.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4246 abstract = {We propose an estimator for the conditional density p(Y |X) that can adapt for asymmetric heavy tails
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4247 which might depend on X. Such estimators have important applications in finance and insurance. We
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4248 draw from Extreme Value Theory the tools to build a hybrid unimodal density having a parameter
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4249 controlling the heaviness of the upper tail. This hybrid is a Gaussian whose upper tail has been
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4250 replaced by a generalized {Pareto} tail. We use this hybrid in a multi-modal mixture in order to obtain
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4251 a nonparametric density estimator that can easily adapt for heavy tailed data. To obtain a conditional
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4252 density estimator, the parameters of the mixture estimator can be seen as functions of X and these
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4253 functions learned. We show experimentally that this approach better models the conditional density in
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4254 terms of likelihood than compared competing algorithms: conditional mixture models with other types
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4255 of components and multivariate nonparametric models.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4256 cat={T},topics={Unsupervised,Mining},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4257 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4258
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4259 @TECHREPORT{tr1284,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4260 author = {Larochelle, Hugo and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4261 title = {Distributed Representation Prediction for Generalization to New Words},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4262 number = {1284},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4263 year = {2006},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4264 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4265 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/dist_rep_pred_tr1284.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4266 abstract = {Learning distributed representations of symbols (e.g. words) has been used in several Natural Language Processing
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4267 systems. Such representations can capture semantic or syntactic similarities between words, which permit to fight
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4268 the curse of dimensionality when considering sequences of such words. Unfortunately, because these representations
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4269 are learned only for a previously determined vocabulary of words, it is not clear how to obtain representations
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4270 for new words. We present here an approach which gets around this problem by considering the distributed representations
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4271 as predictions from low-level or domain-knowledge features of words. We report experiments on a Part
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4272 Of Speech tagging task, which demonstrates the success of this approach in learning meaningful representations and
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4273 in providing improved accuracy, especially for new words.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4274 cat={T},topics={HighDimensional,Language},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4275 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4276
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4277 @TECHREPORT{tr1285,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4278 author = {Grandvalet, Yves and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4279 title = {Hypothesis Testing for Cross-Validation},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4280 number = {1285},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4281 year = {2006},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4282 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4283 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/xv_rho_stat_tr1285.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4284 abstract = {K-fold cross-validation produces variable estimates, whose variance cannot be estimated unbiasedly. However, in practice, one would like to
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4285 provide a figure related to the variability of this estimate. The first part
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4286 of this paper lists a series of restrictive assumptions (on the distribution of
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4287 cross-validation residuals) that allow to derive unbiased estimates. We exhibit three such estimates, corresponding to differing assumptions. Their
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4288 similar expressions however entail almost identical empirical behaviors.
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4289 Then, we look for a conservative test for detecting significant differences
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4290 in performances between two algorithms. Our proposal is based on the
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4291 derivation of the form of a t-statistic parametrized by the correlation of
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4292 residuals between each validation set. Its calibration is compared to the
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4293 usual t-test. While the latter is overconfident in concluding that differences are indeed significant, our test is bound to be more skeptical, with
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4294 smaller type-I error.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4295 cat={T},topics={ModelSelection,Comparative},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4296 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4297
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4298 @TECHREPORT{tr1286,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4299 author = {Erhan, Dumitru and Bengio, Yoshua and {L'Heureux}, Pierre-Jean and Yue, Shi Yi},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4300 title = {Generalizing to a Zero-Data Task: a Computational Chemistry Case Study},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4301 number = {1286},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4302 year = {2006},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4303 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4304 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/mt_qsar_tr1286.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4305 abstract = {We investigate the problem of learning several tasks simultaneously in order to transfer the acquired
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4306 knowledge to a completely new task for which no training data are available. Assuming that the tasks
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4307 share some representation that we can discover efficiently, such a scenario should lead to a better model of
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4308 the new task, as compared to the model that is learned by only using the knowledge of the new task. We
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4309 have evaluated several supervised learning algorithms in order to discover shared representations among
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4310 the tasks defined in a computational chemistry/drug discovery problem. We have cast the problem from
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4311 a statistical learning point of view and set up the general hypotheses that have to be tested in order
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4312 to validate the multi-task learning approach. We have then evaluated the performance of the learning
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4313 algorithms and showed that it is indeed possible to learn a shared representation of the tasks that allows
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4314 to generalize to a new task for which no training data are available. From a theoretical point of view,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4315 our contribution also comprises a modification to the Support Vector Machine algorithm, which can
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4316 produce state-of-the-art results using multi-task learning concepts at its core. From a practical point
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4317 of view, our contribution is that this algorithm can be readily used by pharmaceutical companies for
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4318 virtual screening campaigns.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4319 cat={T},topics={MultiTask,Kernel,Bioinformatic},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4320 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4321
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4322 @INPROCEEDINGS{Turian+al-2009,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4323 author = {Turian, Joseph and Bergstra, James and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4324 title = {Quadratic Features and Deep Architectures for Chunking},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4325 booktitle = {North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL HLT)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4326 year = {2009},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4327 abstract = {We experiment with several chunking models. Deeper architectures achieve better generalization. Quadratic filters, a simplification of theoretical model of V1 complex cells, reliably increase accuracy. In fact, logistic regression with quadratic filters outperforms a standard single hidden layer neural network. Adding quadratic filters to logistic regression is almost as effective as feature engineering. Despite predicting each output label independently, our model is competitive with ones that use previous decisions.}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4328 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4329
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4330 @INPROCEEDINGS{Turian+al-2010,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4331 author = {Turian, Joseph and Ratinov, Lev and Bengio, Yoshua and Roth, Dan},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4332 title = {A preliminary evaluation of word representations for named-entity recognition},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4333 booktitle = {NIPS Workshop on Grammar Induction, Representation of Language and Language Learning},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4334 year = {2009},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4335 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/wordrepresentations-ner.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4336 abstract = {We use different word representations as word features for a named-entity recognition (NER) system with a linear model. This work is part of a larger empirical survey, evaluating different word representations on different NLP tasks. We evaluate Brown clusters, Collobert and Weston (2008) embeddings, and HLBL (Mnih & Hinton, 2009) embeddings of words. All three representations improve accuracy on NER, with the Brown clusters providing a larger improvement than the two embeddings, and the HLBL embeddings more than the Collobert and Weston (2008) embeddings. We also discuss some of the practical issues in using embeddings as features. Brown clusters are simpler than embeddings because they require less hyperparameter tuning.}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4337 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4338
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4339 @INPROCEEDINGS{Turian+Ratinov+Bengio-2010,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4340 author = {Turian, Joseph and Ratinov, Lev and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4341 title = {Word representations: A simple and general method for semi-supervised learning},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4342 booktitle = {Association for Computational Linguistics(ACL2010)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4343 year = {2010}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4344 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4345
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4346 @INPROCEEDINGS{Vincent-Bengio-2003,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4347 author = {Vincent, Pascal and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4348 title = {Manifold Parzen Windows},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4349 year = {2003},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4350 pages = {825--832},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4351 crossref = {NIPS15-shorter},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4352 abstract = {The similarity between objects is a fundamental element of many learning algorithms. Most non-parametric methods take this similarity to be fixed, but much recent work has shown the advantages of learning it, in particular to exploit the local invariances in the data or to capture the possibly non-linear manifold on which most of the data lies. We propose a new non-parametric kernel density estimation method which captures the local structure of an underlying manifold through the leading eigenvectors of regularized local covariance matrices. Experiments in density estimation show significant improvements with respect to Parzen density estimators. The density estimators can also be used within Bayes classifiers, yielding classification rates similar to {SVM}s and much superior to the Parzen classifier.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4353 topics={HighDimensional,Kernel,Unsupervised},cat={C},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4354 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4355
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4356 @TECHREPORT{Vincent-TR1316,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4357 author = {Vincent, Pascal and Larochelle, Hugo and Bengio, Yoshua and Manzagol, Pierre-Antoine},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4358 title = {Extracting and Composing Robust Features with Denoising Autoencoders},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4359 number = {1316},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4360 year = {2008},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4361 institution = {D{\'{e}}partement d'Informatique et Recherche Op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4362 url = {http://www.iro.umontreal.ca/~vincentp/Publications/denoising_autoencoders_tr1316.pdf},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4363 abstract = {Previous work has shown that the difficulties in learning deep generative or discriminative models can be overcome by an initial unsupervised learning step that maps inputs to useful intermediate representations. We introduce and motivate a new training principle for unsupervised learning of a representation based on the idea of making the learned representations robust to partial corruption of the input pattern. This approach can be used to train autoencoders, and these denoising autoencoders can be stacked to initialize deep architectures. The algorithm can be motivated from a manifold learning and information theoretic perspective or from a generative model perspective. Comparative experiments clearly show the surprising advantage of corrupting the input of autoencoders on a pattern classification benchmark suite.}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4364 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4365
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4366 @PHDTHESIS{Vincent2003,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4367 author = {Vincent, Pascal},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4368 title = {Mod{\`{e}}les {\`{a}} Noyaux {\`{a}} Structure Locale},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4369 year = {2003},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4370 school = {D{\'{e}}partement d'Informatique et Recherche Op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4371 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4372
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4373 @ARTICLE{vincent:2001,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4374 author = {Vincent, Pascal and Bengio, Yoshua},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4375 title = {Kernel Matching Pursuit},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4376 journal = {Machine Learning},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4377 year = {2001},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4378 abstract = {We show how Matching Pursuit can be used to build kernel-based solutions to machine-learning problems while keeping control of the sparsity of the solution, and how it can be extended to use non-squared error loss functions. We also deriveMDL motivated generalization bounds for this type of algorithm. Finally, links to boosting algorithms and {RBF} training procedures, as well as extensive experimental comparison with {SVM}s are given, showing comparable results with typically sparser models.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4379 topics={HighDimensional,Kernel},cat={J},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4380 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4381
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4382 @INPROCEEDINGS{VincentPLarochelleH2008,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4383 author = {Vincent, Pascal and Larochelle, Hugo and Bengio, Yoshua and Manzagol, Pierre-Antoine},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4384 title = {Extracting and Composing Robust Features with Denoising Autoencoders},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4385 year = {2008},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4386 pages = {1096--1103},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4387 crossref = {ICML08-shorter},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4388 abstract = {Recently, many applications for Restricted {Boltzmann} Machines (RBMs) have been developed for a large variety of learning problems. However, RBMs are usually used as feature extractors for another learning algorithm or to provide a good initialization
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4389 for deep feed-forward neural network classifiers, and are not considered as a standalone solution to classification problems. In
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4390 this paper, we argue that RBMs provide a self-contained framework for deriving competitive non-linear classifiers. We present an evaluation of different learning algorithms for
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4391 RBMs which aim at introducing a discriminative component to RBM training and improve their performance as classifiers. This
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4392 approach is simple in that RBMs are used directly to build a classifier, rather than as a stepping stone. Finally, we demonstrate how discriminative RBMs can also be successfully employed in a semi-supervised setting.}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4393 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4394
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4395 @TECHREPORT{visualization_techreport,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4396 author = {Erhan, Dumitru and Bengio, Yoshua and Courville, Aaron and Vincent, Pascal},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4397 title = {Visualizing Higher-Layer Features of a Deep Network},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4398 number = {1341},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4399 year = {2009},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4400 institution = {University of Montreal},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4401 abstract = {Deep architectures have demonstrated state-of-the-art results in a variety of
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4402 settings, especially with vision datasets. Beyond the model definitions and the quantitative analyses, there is a need for qualitative comparisons of the solutions learned by various deep architectures. The goal of this paper is to find good qualitative interpretations of high level features represented by such models. To this end, we contrast and compare several techniques applied on Stacked Denoising Autoencoders and Deep Belief Networks, trained on several vision datasets. We show that, perhaps counter-intuitively, such interpretation is possible at the unit level, that it is simple to accomplish and that the results are consistent across various techniques. We hope that such techniques will allow researchers in deep architectures to understand more of how and why deep architectures work}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4403 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4404
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4405 @INPROCEEDINGS{xAISTATS2009-short,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4406 title = {Proc. AISTATS'2009},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4407 booktitle = {Proc. AISTATS'2009},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4408 year = {2009}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4409 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4410
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4411
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4412 @MISC{Yoshua+al-snowbird-2008,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4413 author = {Bengio, Yoshua and Larochelle, Hugo and Turian, Joseph},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4414 title = {Deep Woods},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4415 year = {2008},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4416 howpublished = {Poster presented at the Learning@Snowbird Workshop, Snowbird, USA, 2008}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4417 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4418
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4419 @ARTICLE{Zaccaro-et-al-2005,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4420 author = {Zaccaro, Maria Clara and Boon, Hong and Pattarawarapan, Mookda and Xia, Zebin and Caron, Antoine and {L'Heureux}, Pierre-Jean and Bengio, Yoshua and Burgess, Kevin and Saragori, H. Uri},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4421 title = {Selective Small Molecule Peptidomimetic Ligands of TrkC and TrkA Receptors Afford Discrete or Complete Neurotrophic Activities},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4422 journal = {Chemistry \& Biology},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4423 volume = {12},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4424 number = {9},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4425 year = {2005},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4426 pages = {1015--1028}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4427 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4428
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4429
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4430
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4431 crossreferenced publications:
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4432 @INPROCEEDINGS{ICML09,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4433 editor = {Bottou, {L{\'{e}}on} and Littman, Michael},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4434 title = {Proceedings of the Twenty-sixth International Conference on Machine Learning (ICML'09)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4435 booktitle = {Proceedings of the Twenty-sixth International Conference on Machine Learning (ICML'09)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4436 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4437 publisher = {ACM}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4438 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4439
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4440 @INPROCEEDINGS{NIPS7,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4441 editor = {Tesauro, G. and Touretzky, D. S. and Leen, T. K.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4442 title = {Advances in Neural Information Processing Systems 7 (NIPS'94)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4443 booktitle = {Advances in Neural Information Processing Systems 7 (NIPS'94)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4444 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4445 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4446 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4447
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4448 @INPROCEEDINGS{NIPS6,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4449 editor = {Cowan, J. D. and Tesauro, G. and Alspector, J.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4450 title = {Advances in Neural Information Processing Systems 6 (NIPS'93)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4451 booktitle = {Advances in Neural Information Processing Systems 6 (NIPS'93)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4452 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4453 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4454 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4455
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4456 @INPROCEEDINGS{NIPS8,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4457 editor = {Touretzky, D. S. and Mozer, M. and Hasselmo, M.E.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4458 title = {Advances in Neural Information Processing Systems 8 (NIPS'95)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4459 booktitle = {Advances in Neural Information Processing Systems 8 (NIPS'95)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4460 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4461 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4462 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4463
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4464 @INPROCEEDINGS{NIPS19,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4465 editor = {{Sch{\"{o}}lkopf}, Bernhard and Platt, John and Hoffman, Thomas},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4466 title = {Advances in Neural Information Processing Systems 19 (NIPS'06)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4467 booktitle = {Advances in Neural Information Processing Systems 19 (NIPS'06)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4468 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4469 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4470 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4471
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4472 @INPROCEEDINGS{NIPS10,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4473 editor = {Jordan, M.I. and Kearns, M.J. and Solla, S.A.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4474 title = {Advances in Neural Information Processing Systems 10 (NIPS'97)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4475 booktitle = {Advances in Neural Information Processing Systems 10 (NIPS'97)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4476 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4477 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4478 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4479
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4480 @INPROCEEDINGS{NIPS1,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4481 editor = {Touretzky, D. S.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4482 title = {Advances in Neural Information Processing Systems 1 (NIPS'88)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4483 booktitle = {Advances in Neural Information Processing Systems 1 (NIPS'88)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4484 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4485 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4486 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4487
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4488 @INPROCEEDINGS{NIPS2,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4489 editor = {Touretzky, D. S.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4490 title = {Advances in Neural Information Processing Systems 2 (NIPS'89)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4491 booktitle = {Advances in Neural Information Processing Systems 2 (NIPS'89)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4492 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4493 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4494 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4495
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4496 @INPROCEEDINGS{NIPS4,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4497 editor = {Moody, J. E. and Hanson, S. J. and Lipmann, R. P.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4498 title = {Advances in Neural Information Processing Systems 4 (NIPS'91)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4499 booktitle = {Advances in Neural Information Processing Systems 4 (NIPS'91)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4500 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4501 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4502 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4503
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4504 @INPROCEEDINGS{NIPS12,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4505 editor = {Solla, S.A. and Leen, T. K.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4506 title = {Advances in Neural Information Processing Systems 12 (NIPS'99)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4507 booktitle = {Advances in Neural Information Processing Systems 12 (NIPS'99)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4508 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4509 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4510 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4511
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4512 @INPROCEEDINGS{NIPS16,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4513 editor = {Becker, S. and Saul, L. and {Sch{\"{o}}lkopf}, Bernhard},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4514 title = {Advances in Neural Information Processing Systems 16 (NIPS'03)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4515 booktitle = {Advances in Neural Information Processing Systems 16 (NIPS'03)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4516 year = {-1}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4517 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4518
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4519 @INPROCEEDINGS{NIPS22,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4520 editor = {Bengio, Yoshua and Schuurmans, Dale and Williams, Christopher and Lafferty, John and Culotta, Aron},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4521 title = {Advances in Neural Information Processing Systems 22 (NIPS'09)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4522 booktitle = {Advances in Neural Information Processing Systems 22 (NIPS'09)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4523 year = {-1}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4524 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4525
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4526 @INPROCEEDINGS{NIPS20,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4527 editor = {Platt, John and Koller, D. and Singer, Yoram and Roweis, S.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4528 title = {Advances in Neural Information Processing Systems 20 (NIPS'07)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4529 booktitle = {Advances in Neural Information Processing Systems 20 (NIPS'07)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4530 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4531 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4532 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4533
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4534 @INPROCEEDINGS{xAISTATS2009,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4535 title = {Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS 2009)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4536 booktitle = {Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS 2009)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4537 year = {2009},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4538 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4539
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4540 @INPROCEEDINGS{NIPS9,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4541 editor = {Mozer, M. and Jordan, M.I. and Petsche, T.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4542 title = {Advances in Neural Information Processing Systems 9 (NIPS'96)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4543 booktitle = {Advances in Neural Information Processing Systems 9 (NIPS'96)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4544 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4545 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4546 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4547
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4548 @INPROCEEDINGS{NIPS17,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4549 editor = {Saul, Lawrence K. and Weiss, Yair and Bottou, {L{\'{e}}on}},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4550 title = {Advances in Neural Information Processing Systems 17 (NIPS'04)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4551 booktitle = {Advances in Neural Information Processing Systems 17 (NIPS'04)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4552 year = {-1}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4553 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4554
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4555 @INPROCEEDINGS{ICML08,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4556 editor = {Cohen, William W. and McCallum, Andrew and Roweis, Sam T.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4557 title = {Proceedings of the Twenty-fifth International Conference on Machine Learning (ICML'08)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4558 booktitle = {Proceedings of the Twenty-fifth International Conference on Machine Learning (ICML'08)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4559 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4560 publisher = {ACM}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4561 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4562
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4563 @INPROCEEDINGS{ICML07,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4564 editor = {Ghahramani, Zoubin},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4565 title = {Proceedings of the 24th International Conference on Machine Learning (ICML'07)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4566 booktitle = {Proceedings of the 24th International Conference on Machine Learning (ICML'07)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4567 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4568 publisher = {ACM}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4569 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4570
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4571 @TECHREPORT{DIRO,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4572 title = {DIRO},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4573 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4574 institution = {D{\'{e}}partement d'Informatique et de Recherche Op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4575 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4576
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4577 @INPROCEEDINGS{NIPS18,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4578 editor = {Weiss, Yair and {Sch{\"{o}}lkopf}, Bernhard and Platt, John},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4579 title = {Advances in Neural Information Processing Systems 18 (NIPS'05)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4580 booktitle = {Advances in Neural Information Processing Systems 18 (NIPS'05)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4581 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4582 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4583 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4584
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4585 @INPROCEEDINGS{NIPS13,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4586 editor = {Leen, T. K. and Dietterich, T.G.},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4587 title = {Advances in Neural Information Processing Systems 13 (NIPS'00)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4588 booktitle = {Advances in Neural Information Processing Systems 13 (NIPS'00)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4589 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4590 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4591 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4592
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4593 @INPROCEEDINGS{ICML05,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4594 editor = {Raedt, Luc De and Wrobel, Stefan},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4595 title = {Proceedings of the Twenty-second International Conference on Machine Learning (ICML'05)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4596 booktitle = {Proceedings of the Twenty-second International Conference on Machine Learning (ICML'05)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4597 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4598 publisher = {ACM}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4599 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4600
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4601 @INPROCEEDINGS{ICML06,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4602 editor = {Cohen, William W. and Moore, Andrew},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4603 title = {Proceedings of the Twenty-three International Conference on Machine Learning (ICML'06)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4604 booktitle = {Proceedings of the Twenty-three International Conference on Machine Learning (ICML'06)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4605 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4606 publisher = {ACM}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4607 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4608
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4609 @INPROCEEDINGS{NIPS15,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4610 editor = {Becker, S. and Thrun, Sebastian},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4611 title = {Advances in Neural Information Processing Systems 15 (NIPS'02)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4612 booktitle = {Advances in Neural Information Processing Systems 15 (NIPS'02)},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4613 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4614 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4615 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4616
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4617 @INPROCEEDINGS{ICML01-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4618 title = {ICML'01},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4619 booktitle = {ICML'01},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4620 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4621 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4622 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4623 @INPROCEEDINGS{ICML02-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4624 title = {ICML'02},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4625 booktitle = {ICML'02},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4626 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4627 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4628 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4629 @INPROCEEDINGS{ICML03-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4630 title = {ICML'03},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4631 booktitle = {ICML'03},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4632 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4633 publisher = {AAAI Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4634 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4635 @INPROCEEDINGS{ICML04-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4636 title = {ICML'04},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4637 booktitle = {ICML'04},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4638 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4639 publisher = {ACM}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4640 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4641 @INPROCEEDINGS{ICML05-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4642 title = {ICML'05},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4643 booktitle = {ICML'05},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4644 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4645 publisher = {ACM}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4646 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4647 @INPROCEEDINGS{ICML06-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4648 title = {ICML'06},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4649 booktitle = {ICML'06},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4650 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4651 publisher = {ACM}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4652 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4653 @INPROCEEDINGS{ICML07-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4654 title = {ICML'07},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4655 booktitle = {ICML'07},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4656 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4657 publisher = {ACM}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4658 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4659 @INPROCEEDINGS{ICML08-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4660 title = {ICML'08},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4661 booktitle = {ICML'08},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4662 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4663 publisher = {ACM}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4664 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4665 @INPROCEEDINGS{ICML09-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4666 title = {ICML'09},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4667 booktitle = {ICML'09},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4668 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4669 publisher = {ACM}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4670 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4671 @INPROCEEDINGS{ICML96-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4672 title = {ICML'96},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4673 booktitle = {ICML'96},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4674 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4675 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4676 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4677 @INPROCEEDINGS{ICML97-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4678 title = {ICML'97},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4679 booktitle = {ICML'97},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4680 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4681 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4682 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4683 @INPROCEEDINGS{ICML98-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4684 title = {ICML'98},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4685 booktitle = {ICML'98},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4686 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4687 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4688 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4689 @INPROCEEDINGS{ICML99-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4690 title = {ICML'99},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4691 booktitle = {ICML'99},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4692 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4693 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4694 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4695 @INPROCEEDINGS{NIPS1-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4696 title = {NIPS'88},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4697 booktitle = {NIPS 1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4698 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4699 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4700 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4701 @INPROCEEDINGS{NIPS10-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4702 title = {NIPS'97},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4703 booktitle = {NIPS 10},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4704 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4705 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4706 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4707 @INPROCEEDINGS{NIPS11-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4708 title = {NIPS'98},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4709 booktitle = {NIPS 11},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4710 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4711 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4712 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4713 @INPROCEEDINGS{NIPS12-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4714 title = {NIPS'99},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4715 booktitle = {NIPS 12},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4716 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4717 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4718 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4719 @INPROCEEDINGS{NIPS13-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4720 title = {NIPS'00},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4721 booktitle = {NIPS 13},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4722 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4723 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4724 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4725 @INPROCEEDINGS{NIPS14-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4726 title = {NIPS'01},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4727 booktitle = {NIPS 14},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4728 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4729 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4730 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4731 @INPROCEEDINGS{NIPS15-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4732 title = {NIPS'02},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4733 booktitle = {NIPS 15},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4734 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4735 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4736 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4737 @INPROCEEDINGS{NIPS16-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4738 title = {NIPS'03},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4739 booktitle = {NIPS 16},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4740 year = {-1}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4741 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4742 @INPROCEEDINGS{NIPS17-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4743 title = {NIPS'04},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4744 booktitle = {NIPS 17},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4745 year = {-1}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4746 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4747 @INPROCEEDINGS{NIPS18-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4748 title = {NIPS'05},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4749 booktitle = {NIPS 18},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4750 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4751 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4752 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4753 @INPROCEEDINGS{NIPS19-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4754 title = {NIPS'06},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4755 booktitle = {NIPS 19},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4756 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4757 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4758 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4759 @INPROCEEDINGS{NIPS2-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4760 title = {NIPS'89},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4761 booktitle = {NIPS 2},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4762 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4763 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4764 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4765 @INPROCEEDINGS{NIPS20-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4766 title = {NIPS'07},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4767 booktitle = {NIPS 20},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4768 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4769 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4770 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4771 @INPROCEEDINGS{NIPS21-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4772 title = {NIPS'08},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4773 booktitle = {NIPS 21},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4774 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4775 publisher = {Nips Foundation (http://books.nips.cc)}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4776 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4777 @INPROCEEDINGS{NIPS22-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4778 title = {NIPS'09},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4779 booktitle = {NIPS 22},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4780 year = {-1}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4781 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4782 @INPROCEEDINGS{NIPS3-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4783 title = {NIPS'90},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4784 booktitle = {NIPS 3},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4785 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4786 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4787 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4788 @INPROCEEDINGS{NIPS4-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4789 title = {NIPS'91},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4790 booktitle = {NIPS 4},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4791 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4792 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4793 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4794 @INPROCEEDINGS{NIPS5-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4795 title = {NIPS'92},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4796 booktitle = {NIPS 5},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4797 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4798 publisher = {Morgan Kaufmann}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4799 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4800 @INPROCEEDINGS{NIPS6-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4801 title = {NIPS'93},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4802 booktitle = {NIPS 6},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4803 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4804 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4805 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4806 @INPROCEEDINGS{NIPS7-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4807 title = {NIPS'94},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4808 booktitle = {NIPS 7},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4809 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4810 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4811 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4812 @INPROCEEDINGS{NIPS8-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4813 title = {NIPS'95},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4814 booktitle = {NIPS 8},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4815 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4816 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4817 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4818 @INPROCEEDINGS{NIPS9-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4819 title = {NIPS'96},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4820 booktitle = {NIPS 9},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4821 year = {-1},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4822 publisher = {MIT Press}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4823 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4824 @INPROCEEDINGS{xAISTATS2009-shorter,
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4825 title = {AISTATS'2009},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4826 booktitle = {AISTATS'2009},
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4827 year = {-1}
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4828 }
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4829
b1be957dd1be Added mlj_submission to group every file needed for that.
fsavard
parents:
diff changeset
4830