Mercurial > ift6266
annotate writeup/aigaion-shorter.bib @ 640:9910b921c9a6
added rules depency again *.bib file.
author | Frederic Bastien <nouiz@nouiz.org> |
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date | Mon, 21 Mar 2011 11:26:56 -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 02 November 2010 04:10:50 PM |
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3 @MASTERSTHESIS{, |
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4 author = {Breuleux, Olivier}, |
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5 title = {{\'{E}}chantillonnage dynamique de champs markoviens}, |
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6 year = {2010}, |
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7 school = {Universit{\'{e}} de Montr{\'{e}}al} |
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8 } |
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9 |
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10 @PHDTHESIS{, |
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11 author = {Rivest, Fran{\c c}ois}, |
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12 title = {Mod{\`{e}}le informatique du coapprentissage des ganglions de la base et du cortex : L’apprentissage par renforcement et le d{\'{e}}veloppement de repr{\'{e}}sentations}, |
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13 year = {2009}, |
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14 school = {Universit{\'{e}} de Montr{\'{e}}al, D{\'{e}}partement d’informatique et de recherche op{\'{e}}rationnelle}, |
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15 abstract = {English follow: |
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16 |
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17 Tout au long de la vie, le cerveau d{\'{e}}veloppe des repr{\'{e}}sentations de son |
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18 environnement permettant {\`{a}} l’individu d’en tirer meilleur profit. Comment ces |
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19 repr{\'{e}}sentations se d{\'{e}}veloppent-elles pendant la qu{\^{e}}te de r{\'{e}}compenses demeure un |
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20 myst{\`{e}}re. Il est raisonnable de penser que le cortex est le si{\`{e}}ge de ces repr{\'{e}}sentations |
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21 et que les ganglions de la base jouent un r{\^{o}}le important dans la maximisation des |
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22 r{\'{e}}compenses. En particulier, les neurones dopaminergiques semblent coder un signal |
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23 d’erreur de pr{\'{e}}diction de r{\'{e}}compense. Cette th{\`{e}}se {\'{e}}tudie le probl{\`{e}}me en construisant, |
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24 {\`{a}} l’aide de l’apprentissage machine, un mod{\`{e}}le informatique int{\'{e}}grant de nombreuses |
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25 {\'{e}}vidences neurologiques. |
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26 Apr{\`{e}}s une introduction au cadre math{\'{e}}matique et {\`{a}} quelques algorithmes de |
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27 l’apprentissage machine, un survol de l’apprentissage en psychologie et en |
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28 neuroscience et une revue des mod{\`{e}}les de l’apprentissage dans les ganglions de la |
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29 base, la th{\`{e}}se comporte trois articles. Le premier montre qu’il est possible |
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30 d’apprendre {\`{a}} maximiser ses r{\'{e}}compenses tout en d{\'{e}}veloppant de meilleures |
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31 repr{\'{e}}sentations des entr{\'{e}}es. Le second article porte sur l'important probl{\`{e}}me toujours |
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32 non r{\'{e}}solu de la repr{\'{e}}sentation du temps. Il d{\'{e}}montre qu’une repr{\'{e}}sentation du temps |
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33 peut {\^{e}}tre acquise automatiquement dans un r{\'{e}}seau de neurones artificiels faisant |
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34 office de m{\'{e}}moire de travail. La repr{\'{e}}sentation d{\'{e}}velopp{\'{e}}e par le mod{\`{e}}le ressemble |
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35 beaucoup {\`{a}} l’activit{\'{e}} de neurones corticaux dans des t{\^{a}}ches similaires. De plus, le |
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36 mod{\`{e}}le montre que l’utilisation du signal d’erreur de r{\'{e}}compense peut acc{\'{e}}l{\'{e}}rer la |
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37 construction de ces repr{\'{e}}sentations temporelles. Finalement, il montre qu’une telle |
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38 repr{\'{e}}sentation acquise automatiquement dans le cortex peut fournir l’information |
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39 n{\'{e}}cessaire aux ganglions de la base pour expliquer le signal dopaminergique. Enfin, |
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40 le troisi{\`{e}}me article {\'{e}}value le pouvoir explicatif et pr{\'{e}}dictif du mod{\`{e}}le sur diff{\'{e}}rentes |
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41 situations comme la pr{\'{e}}sence ou l’absence d’un stimulus (conditionnement classique |
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42 ou de trace) pendant l’attente de la r{\'{e}}compense. En plus de faire des pr{\'{e}}dictions tr{\`{e}}s |
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43 int{\'{e}}ressantes en lien avec la litt{\'{e}}rature sur les intervalles de temps, l’article r{\'{e}}v{\`{e}}le |
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44 certaines lacunes du mod{\`{e}}le qui devront {\^{e}}tre am{\'{e}}lior{\'{e}}es. |
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45 Bref, cette th{\`{e}}se {\'{e}}tend les mod{\`{e}}les actuels de l’apprentissage des ganglions de |
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46 la base et du syst{\`{e}}me dopaminergique au d{\'{e}}veloppement concurrent de |
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47 repr{\'{e}}sentations temporelles dans le cortex et aux interactions de ces deux structures. |
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48 |
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49 Throughout lifetime, the brain develops abstract representations of its |
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50 environment that allow the individual to maximize his benefits. How these |
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51 representations are developed while trying to acquire rewards remains a mystery. It is |
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52 reasonable to assume that these representations arise in the cortex and that the basal |
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53 ganglia are playing an important role in reward maximization. In particular, |
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54 dopaminergic neurons appear to code a reward prediction error signal. This thesis |
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55 studies the problem by constructing, using machine learning tools, a computational |
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56 model that incorporates a number of relevant neurophysiological findings. |
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57 After an introduction to the machine learning framework and to some of its |
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58 algorithms, an overview of learning in psychology and neuroscience, and a review of |
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59 models of learning in the basal ganglia, the thesis comprises three papers. The first |
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60 article shows that it is possible to learn a better representation of the inputs while |
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61 learning to maximize reward. The second paper addresses the important and still |
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62 unresolved problem of the representation of time in the brain. The paper shows that a |
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63 time representation can be acquired automatically in an artificial neural network |
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64 acting like a working memory. The representation learned by the model closely |
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65 resembles the activity of cortical neurons in similar tasks. Moreover, the model shows |
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66 that the reward prediction error signal could accelerate the development of the |
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67 temporal representation. Finally, it shows that if such a learned representation exists |
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68 in the cortex, it could provide the necessary information to the basal ganglia to |
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69 explain the dopaminergic signal. The third article evaluates the explanatory and |
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70 predictive power of the model on the effects of differences in task conditions such as |
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71 the presence or absence of a stimulus (classical versus trace conditioning) while |
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72 waiting for the reward. Beyond making interesting predictions relevant to the timing |
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73 literature, the paper reveals some shortcomings of the model that will need to be |
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74 resolved. |
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75 In summary, this thesis extends current models of reinforcement learning of |
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76 the basal ganglia and the dopaminergic system to the concurrent development of |
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77 representation in the cortex and to the interactions between these two regions.} |
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78 } |
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79 |
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80 @MASTERSTHESIS{, |
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81 author = {Wood, Sean}, |
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82 title = {Non-negative matrix decomposition approaches to frequency domain analysis of music audio signals}, |
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83 year = {2010}, |
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84 school = {Universit{\'{e}} de Montr{\'{e}}al} |
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85 } |
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86 |
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87 @TECHREPORT{ARXIV-2010, |
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88 author = {Bastien, Fr{\'{e}}d{\'{e}}ric and Bengio, Yoshua and Bergeron, Arnaud and Boulanger-Lewandowski, Nicolas and Breuel, Thomas and Chherawala, Youssouf and Cisse, Moustapha and C{\^{o}}t{\'{e}}, Myriam and Erhan, Dumitru and Eustache, Jeremy and Glorot, Xavier and Muller, Xavier and Pannetier Lebeuf, Sylvain and Pascanu, Razvan and Rifai, Salah and Savard, Fran{\c c}ois and Sicard, Guillaume}, |
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89 keywords = {Computer Vision and Pattern Recognition, Learning, Neural and Evolutionary Computing}, |
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90 title = {Deep Self-Taught Learning for Handwritten Character Recognition}, |
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91 number = {1353}, |
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92 year = {2010}, |
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93 institution = {University of Montr{\'{e}}al}, |
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94 abstract = {Recent theoretical and empirical work in statistical machine learning has demonstrated the importance of learning algorithms for deep architectures, i.e., function classes obtained by composing multiple non-linear transformations. Self-taught learning (exploiting unlabeled examples or examples from other distributions) has already been applied to deep learners, but mostly to show the advantage of unlabeled examples. Here we explore the advantage brought by {\em out-of-distribution examples}. For this purpose we developed a powerful generator of stochastic variations and noise processes for character images, including not only affine transformations but also slant, local elastic deformations, changes in thickness, background images, grey level changes, contrast, occlusion, and various types of noise. The out-of-distribution examples are obtained from these highly distorted images or by including examples of object classes different from those in the target test set. We show that {\em deep learners benefit more from out-of-distribution examples than a corresponding shallow learner}, at least in the area of handwritten character recognition. In fact, we show that they beat previously published results and reach human-level performance on both handwritten digit classification and 62-class handwritten character recognition.} |
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95 } |
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96 |
498
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97 @INPROCEEDINGS{Attardi+al-2009, |
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98 author = {Attardi, Giuseppe and Dell'Orletta, Felice and Simi, Maria and Turian, Joseph}, |
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99 keywords = {classifier, dependency parsing, natural language, parser, perceptron}, |
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100 title = {Accurate Dependency Parsing with a Stacked Multilayer Perceptron}, |
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101 booktitle = {Proceeding of Evalita 2009}, |
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102 series = {LNCS}, |
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103 year = {2009}, |
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104 publisher = {Springer}, |
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105 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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106 } |
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107 |
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108 @INPROCEEDINGS{Bengio+al-2009, |
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109 author = {Bengio, Yoshua and Louradour, Jerome and Collobert, Ronan and Weston, Jason}, |
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110 title = {Curriculum Learning}, |
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111 year = {2009}, |
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112 crossref = {ICML09-shorter}, |
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113 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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114 } |
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115 |
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116 @TECHREPORT{Bengio+al-2009-TR, |
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117 author = {Bengio, Yoshua and Louradour, Jerome and Collobert, Ronan and Weston, Jason}, |
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118 title = {Curriculum Learning}, |
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119 number = {1330}, |
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120 year = {2009}, |
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121 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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122 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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123 } |
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124 |
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125 @MISC{Bengio+al-patent-2000, |
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126 author = {Bengio, Yoshua and Bottou, {L{\'{e}}on} and {LeCun}, Yann}, |
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127 title = {Module for constructing trainable modular network in which each module outputs and inputs data structured as a graph}, |
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128 year = {2000}, |
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129 howpublished = {U.S. Patent 6,128,606, October 3} |
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130 } |
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131 |
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132 @MISC{Bengio+al-patent-2001, |
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133 author = {Bengio, Yoshua and Bottou, {L{\'{e}}on} and G. Howard, Paul}, |
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134 title = {Z-Coder : a fast adaptive binary arithmetic coder}, |
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135 year = {2001}, |
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136 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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137 } |
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138 |
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139 @MISC{Bengio+al-patent-94, |
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140 author = {Bengio, Yoshua and {LeCun}, Yann and Nohl, Craig and Burges, Chris}, |
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141 title = {Visitor Registration System Using Automatic Handwriting Recognition}, |
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142 year = {1994}, |
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143 howpublished = {Patent submitted in the U.S.A. in October 1994, submission number 1-16-18-1} |
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144 } |
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145 |
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146 @INCOLLECTION{Bengio+al-spectral-2006, |
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147 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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148 editor = {Guyon, Isabelle and Gunn, Steve and Nikravesh, Masoud and Zadeh, Lofti}, |
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149 title = {Spectral Dimensionality Reduction}, |
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150 booktitle = {Feature Extraction, Foundations and Applications}, |
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151 year = {2006}, |
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152 publisher = {Springer}, |
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153 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/eigenfn_chapter.pdf}, |
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154 abstract = {In this chapter, we study and put under a common framework a number |
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155 of non-linear dimensionality reduction methods, such as Locally Linear Embedding, |
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156 Isomap, Laplacian eigenmaps and kernel {PCA}, which are based |
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157 on performing an eigen-decomposition (hence the name "spectral"). That |
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158 framework also includes classical methods such as {PCA} and metric multidimensional |
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159 scaling ({MDS}). It also includes the data transformation step used |
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160 in spectral clustering. We show that in all of these cases the learning algorithm |
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161 estimates the principal eigenfunctions of an operator that depends on |
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162 the unknown data density and on a kernel that is not necessarily positive |
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163 semi-definite. This helps to generalize some of these algorithms so as to predict |
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164 an embedding for out-of-sample examples without having to retrain the |
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165 model. It also makes it more transparent what these algorithm are minimizing |
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166 on the empirical data and gives a corresponding notion of generalization |
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167 error.}, |
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168 cat={B},topics={HighDimensional,Kernel,Unsupervised}, |
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169 } |
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170 |
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171 @INCOLLECTION{Bengio+al-ssl-2006, |
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172 author = {Bengio, Yoshua and Delalleau, Olivier and Le Roux, Nicolas}, |
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173 editor = {Chapelle, Olivier and {Sch{\"{o}}lkopf}, Bernhard and Zien, Alexander}, |
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174 title = {Label Propagation and Quadratic Criterion}, |
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175 booktitle = {Semi-Supervised Learning}, |
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176 year = {2006}, |
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177 pages = {193--216}, |
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178 publisher = {{MIT} Press}, |
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179 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/bengio_ssl.pdf}, |
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180 abstract = {Various graph-based algorithms for semi-supervised learning have been proposed in |
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181 the recent literature. They rely on the idea of building a graph whose nodes are |
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182 data points (labeled and unlabeled) and edges represent similarities between points. |
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183 Known labels are used to propagate information through the graph in order to label |
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184 all nodes. In this chapter, we show how these different algorithms can be cast into |
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185 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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186 points). The cost criterion naturally leads to an extension of such algorithms to |
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187 the inductive setting, where one obtains test samples one at a time: the derived |
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188 induction formula can be evaluated in O(n) time, which is much more efficient |
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189 than solving again exactly the linear system (which in general costs O(kn2) time |
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190 for a sparse graph where each data point has k neighbors). We also use this inductive |
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191 formula to show that when the similarity between points satisfies a locality property, |
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192 then the algorithms are plagued by the curse of dimensionality, with respect to the |
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193 dimensionality of an underlying manifold.}, |
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194 cat={B},topics={Unsupervised}, |
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195 } |
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196 |
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197 @TECHREPORT{Bengio+al-treecurse-2007, |
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198 author = {Bengio, Yoshua and Delalleau, Olivier and Simard, Clarence}, |
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199 title = {Decision Trees do not Generalize to New Variations}, |
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200 number = {1304}, |
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201 year = {2007}, |
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202 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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203 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/bengio+al-tr1304.pdf} |
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204 } |
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205 |
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206 @INPROCEEDINGS{Bengio+Bengio96, |
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207 author = {Bengio, Samy and Bengio, Yoshua}, |
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208 editor = {Xu, L.}, |
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209 title = {An {EM} Algorithm for Asynchronous Input/Output Hidden {M}arkov Models}, |
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210 booktitle = {International Conference On Neural Information Processing}, |
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211 year = {1996}, |
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212 pages = {328--334}, |
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213 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/iconip96.pdf}, |
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214 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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215 topics={Markov},cat={C}, |
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216 } |
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217 |
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218 @INCOLLECTION{Bengio+chapter2007, |
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219 author = {Bengio, Yoshua and {LeCun}, Yann}, |
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220 editor = {Bottou, {L{\'{e}}on} and Chapelle, Olivier and DeCoste, D. and Weston, J.}, |
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221 title = {Scaling Learning Algorithms towards {AI}}, |
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222 booktitle = {Large Scale Kernel Machines}, |
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223 year = {2007}, |
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224 publisher = {MIT Press}, |
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225 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/bengio+lecun_chapter2007.pdf}, |
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226 abstract = {One long-term goal of machine learning research is to produce methods that |
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227 are applicable to highly complex tasks, such as perception (vision, audition), reasoning, |
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228 intelligent control, and other artificially intelligent behaviors. We argue |
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229 that in order to progress toward this goal, the Machine Learning community must |
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230 endeavor to discover algorithms that can learn highly complex functions, with minimal |
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231 need for prior knowledge, and with minimal human intervention. We present |
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232 mathematical and empirical evidence suggesting that many popular approaches |
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233 to non-parametric learning, particularly kernel methods, are fundamentally limited |
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234 in their ability to learn complex high-dimensional functions. Our analysis |
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235 focuses on two problems. First, kernel machines are shallow architectures, in |
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236 which one large layer of simple template matchers is followed by a single layer |
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237 of trainable coefficients. We argue that shallow architectures can be very inefficient |
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238 in terms of required number of computational elements and examples. Second, |
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239 we analyze a limitation of kernel machines with a local kernel, linked to the |
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240 curse of dimensionality, that applies to supervised, unsupervised (manifold learning) |
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241 and semi-supervised kernel machines. Using empirical results on invariant |
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242 image recognition tasks, kernel methods are compared with deep architectures, in |
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243 which lower-level features or concepts are progressively combined into more abstract |
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244 and higher-level representations. We argue that deep architectures have the |
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245 potential to generalize in non-local ways, i.e., beyond immediate neighbors, and |
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246 that this is crucial in order to make progress on the kind of complex tasks required |
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247 for artificial intelligence.}, |
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248 cat={B},topics={HighDimensional}, |
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249 } |
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250 |
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251 @ARTICLE{Bengio+Delalleau-2009, |
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252 author = {Bengio, Yoshua and Delalleau, Olivier}, |
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253 title = {Justifying and Generalizing Contrastive Divergence}, |
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254 journal = {Neural Computation}, |
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255 volume = {21}, |
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256 number = {6}, |
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257 year = {2009}, |
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258 pages = {1601--1621}, |
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259 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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260 } |
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261 |
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262 @TECHREPORT{Bengio+Delalleau-TR2007, |
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263 author = {Bengio, Yoshua and Delalleau, Olivier}, |
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264 keywords = {Contrastive Divergence, Restricted {Boltzmann} Machine}, |
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265 title = {Justifying and Generalizing Contrastive Divergence}, |
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266 number = {1311}, |
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267 year = {2007}, |
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268 institution = {D{\'{e}}partement d'Informatique et Recherche Op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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269 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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270 } |
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271 |
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272 @INPROCEEDINGS{Bengio+DeMori88, |
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273 author = {Bengio, Yoshua and De Mori, Renato}, |
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274 title = {Use of neural networks for the recognition of place of articulation}, |
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275 booktitle = {International Conference on Acoustics, Speech and Signal Processing}, |
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276 year = {1988}, |
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277 pages = {103--106}, |
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278 topics={Speech},cat={C}, |
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279 } |
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280 |
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281 @INPROCEEDINGS{Bengio+DeMori89, |
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282 author = {Bengio, Yoshua and Cardin, Regis and Cosi, Piero and De Mori, Renato}, |
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283 title = {Speech coding with multi-layer networks}, |
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284 booktitle = {International Conference on Acoustics, Speech and Signal Processing}, |
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285 year = {1989}, |
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286 pages = {164--167}, |
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287 topics={Speech},cat={C}, |
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288 } |
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289 |
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290 @INCOLLECTION{Bengio+DeMori90a, |
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291 author = {Bengio, Yoshua and De Mori, Renato}, |
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292 editor = {Sethi, I. K. and Jain, A. K.}, |
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293 title = {Connectionist models and their application to automatic speech recognition}, |
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294 booktitle = {Artificial Neural Networks and Statistical Pattern Recognition: Old and New Connections}, |
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295 year = {1990}, |
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296 pages = {175--192}, |
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297 publisher = {Elsevier, Machine Intelligence and Pattern Recognition Series}, |
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298 topics={Speech},cat={B}, |
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299 } |
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300 |
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301 @ARTICLE{Bengio+Frasconi-jair95, |
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302 author = {Bengio, Yoshua and Frasconi, Paolo}, |
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303 title = {Diffusion of Context and Credit Information in {M}arkovian Models}, |
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304 journal = {Journal of Artificial Intelligence Research}, |
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305 volume = {3}, |
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306 year = {1995}, |
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307 pages = {249--270}, |
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308 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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309 topics={Markov,LongTerm},cat={J}, |
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310 } |
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311 |
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312 @INPROCEEDINGS{Bengio+Frasconi-nips7-diffuse, |
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313 author = {Bengio, Yoshua and Frasconi, Paolo}, |
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314 title = {Diffusion of Credit in {M}arkovian Models}, |
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315 year = {1995}, |
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316 pages = {553--560}, |
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317 crossref = {NIPS7-shorter}, |
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318 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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319 topics={Markov},cat={C}, |
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320 } |
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321 |
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322 @INPROCEEDINGS{Bengio+Frasconi-nips7-iohmms, |
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323 author = {Bengio, Yoshua and Frasconi, Paolo}, |
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324 title = {An Input/Output {HMM} Architecture}, |
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325 year = {1995}, |
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326 pages = {427--434}, |
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327 crossref = {NIPS7-shorter}, |
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328 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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329 topics={Markov},cat={C}, |
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330 } |
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331 |
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332 @INPROCEEDINGS{Bengio+Frasconi-nips94, |
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333 author = {Bengio, Yoshua and Frasconi, Paolo}, |
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334 title = {Credit Assignment through Time: Alternatives to Backpropagation}, |
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335 year = {1994}, |
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336 pages = {75--82}, |
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337 crossref = {NIPS6-shorter}, |
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338 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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339 topics={LongTerm},cat={C}, |
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340 } |
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341 |
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342 @ARTICLE{Bengio+Pouliot90, |
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343 author = {Bengio, Yoshua and Pouliot, Yannick}, |
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344 title = {Efficient recognition of immunoglobulin domains from amino-acid sequences using a neural network}, |
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345 journal = {Computer Applications in the Biosciences}, |
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346 volume = {6}, |
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347 number = {2}, |
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348 year = {1990}, |
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349 pages = {319--324}, |
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350 topics={Bioinformatic,PriorKnowledge},cat={J}, |
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351 } |
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352 |
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353 @INPROCEEDINGS{Bengio+Senecal-2003, |
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354 author = {Bengio, Yoshua and S{\'{e}}n{\'{e}}cal, Jean-S{\'{e}}bastien}, |
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355 title = {Quick Training of Probabilistic Neural Nets by Importance Sampling}, |
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356 booktitle = {Proceedings of the conference on Artificial Intelligence and Statistics (AISTATS)}, |
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357 year = {2003}, |
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358 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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359 } |
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360 |
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361 @ARTICLE{Bengio+Senecal-2008, |
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362 author = {Bengio, Yoshua and S{\'{e}}n{\'{e}}cal, Jean-S{\'{e}}bastien}, |
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363 keywords = {Energy-based models, fast training, importance sampling, language modeling, Monte Carlo methods, probabilistic neural networks}, |
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364 title = {Adaptive Importance Sampling to Accelerate Training of a Neural Probabilistic Language Model}, |
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365 journal = {IEEE Transactions on Neural Networks}, |
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366 volume = {19}, |
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367 number = {4}, |
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368 year = {2008}, |
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369 pages = {713--722}, |
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370 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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371 } |
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372 |
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373 @INCOLLECTION{Bengio-2007, |
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374 author = {Bengio, Yoshua}, |
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375 editor = {Cisek, Paul and Kalaska, John and Drew, Trevor}, |
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376 title = {On the Challenge of Learning Complex Functions}, |
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377 booktitle = {Computational Neuroscience: Theoretical Insights into Brain Function}, |
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378 series = {Progress in Brain Research}, |
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379 year = {2007}, |
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380 publisher = {Elsevier}, |
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381 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/PBR_chapter.pdf}, |
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382 abstract = {A common goal of computational neuroscience and of artificial intelligence |
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383 research based on statistical learning algorithms is the discovery and |
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384 understanding of computational principles that could explain what we |
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385 consider adaptive intelligence, in animals as well as in machines. This |
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386 chapter focuses on what is required for the learning of complex behaviors. We |
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387 believe it involves the learning of highly varying functions, in a |
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388 mathematical sense. We bring forward two types of arguments which convey |
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389 the message that many currently popular machine learning approaches to |
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390 learning flexible functions have fundamental limitations that render them |
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391 inappropriate for learning highly varying functions. The first issue |
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392 concerns the representation of such functions with what we call shallow model |
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393 architectures. We discuss limitations of shallow architectures, such as |
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394 so-called kernel machines, boosting algorithms, and one-hidden-layer artificial neural |
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395 networks. The second issue is more focused and concerns kernel machines |
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396 with a local kernel (the type used most often in practice), |
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397 that act like a collection of template matching units. We present |
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398 mathematical results on such computational architectures showing that they |
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399 have a limitation similar to those already proved for older non-parametric |
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400 methods, and connected to the so-called curse of dimensionality. Though it has long |
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401 been believed that efficient learning in deep architectures is difficult, |
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402 recently proposed computational principles for learning in deep architectures |
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403 may offer a breakthrough.} |
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404 } |
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405 |
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406 @ARTICLE{Bengio-2009, |
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407 author = {Bengio, Yoshua}, |
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408 title = {Learning deep architectures for {AI}}, |
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409 journal = {Foundations and Trends in Machine Learning}, |
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410 volume = {2}, |
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411 number = {1}, |
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412 year = {2009}, |
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413 pages = {1--127}, |
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414 note = {Also published as a book. Now Publishers, 2009.}, |
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415 abstract = {Theoretical results suggest that in order to learn the kind of |
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416 complicated functions that can represent high-level abstractions (e.g. in |
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417 vision, language, and other AI-level tasks), one may need {\insist deep |
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418 architectures}. Deep architectures are composed of multiple levels of non-linear |
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419 operations, such as in neural nets with many hidden layers or in complicated |
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420 propositional formulae re-using many sub-formulae. Searching the |
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421 parameter space of deep architectures is a difficult task, but |
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422 learning algorithms such as those for Deep Belief Networks have recently been proposed |
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423 to tackle this problem with notable success, beating the state-of-the-art |
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424 in certain areas. This paper discusses the motivations and principles regarding |
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425 learning algorithms for deep architectures, in particular those exploiting as |
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426 building blocks unsupervised learning of single-layer models such as Restricted {Boltzmann} Machines, |
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427 used to construct deeper models such as Deep Belief Networks.} |
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428 } |
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429 |
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430 @TECHREPORT{Bengio-96-TR, |
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431 author = {Bengio, Yoshua}, |
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432 title = {Using a Financial Training Criterion Rather than a Prediction Criterion}, |
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433 number = {\#1019}, |
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434 year = {1996}, |
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435 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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436 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/bengioy_TR1019.pdf}, |
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437 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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438 topics={Finance,Discriminant},cat={T}, |
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439 } |
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440 |
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441 @BOOK{bengio-book96, |
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442 author = {Bengio, Yoshua}, |
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443 title = {Neural Networks for Speech and Sequence Recognition}, |
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444 year = {1996}, |
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445 publisher = {International Thompson Computer Press}, |
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446 topics={Speech},cat={B}, |
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447 } |
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448 |
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449 @TECHREPORT{Bengio-convex-05, |
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450 author = {Bengio, Yoshua and Le Roux, Nicolas and Vincent, Pascal and Delalleau, Olivier and Marcotte, Patrice}, |
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451 title = {Convex neural networks}, |
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452 number = {1263}, |
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453 year = {2005}, |
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454 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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455 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/TR1263.pdf}, |
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456 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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457 topics={Boosting},cat={T}, |
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458 } |
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459 |
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460 @ARTICLE{Bengio-decision-trees10, |
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461 author = {Bengio, Yoshua and Delalleau, Olivier and Simard, Clarence}, |
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462 title = {Decision Trees do not Generalize to New Variations}, |
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463 journal = {Computational Intelligence}, |
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464 year = {2010}, |
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465 note = {To appear} |
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466 } |
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467 |
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468 @ARTICLE{bengio-demori89, |
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469 author = {Bengio, Yoshua and De Mori, Renato}, |
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470 title = {Use of multilayer networks for the recognition of phonetic features and phonemes}, |
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471 journal = {Computational Intelligence}, |
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472 volume = {5}, |
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473 year = {1989}, |
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474 pages = {134--141}, |
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475 topics={Speech},cat={J}, |
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476 } |
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477 |
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478 @ARTICLE{Bengio-eigen-NC2004, |
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479 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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480 title = {Learning eigenfunctions links spectral embedding and kernel {PCA}}, |
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481 journal = {Neural Computation}, |
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482 volume = {16}, |
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483 number = {10}, |
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484 year = {2004}, |
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485 pages = {2197--2219}, |
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486 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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487 topics={HighDimensional,Kernel,Unsupervised},cat={J}, |
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488 } |
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489 |
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490 @INPROCEEDINGS{Bengio-Gingras-nips8, |
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491 author = {Bengio, Yoshua and Gingras, Fran{\c c}ois}, |
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492 title = {Recurrent Neural Networks for Missing or Asynchronous Data}, |
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493 year = {1996}, |
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494 pages = {395--401}, |
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495 crossref = {NIPS8-shorter}, |
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496 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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497 topics={Finance,Missing},cat={C}, |
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498 } |
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499 |
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500 @ARTICLE{Bengio-Grandvalet-JMLR-04, |
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501 author = {Bengio, Yoshua and Grandvalet, Yves}, |
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502 title = {No Unbiased Estimator of the Variance of K-Fold Cross-Validation}, |
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503 volume = {5}, |
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504 year = {2004}, |
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505 pages = {1089--1105}, |
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506 crossref = {JMLR-shorter}, |
498
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507 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 dont 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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508 topics={Comparative},cat={J}, |
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509 } |
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510 |
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511 @TECHREPORT{bengio-hyper-TR99, |
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512 author = {Bengio, Yoshua}, |
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513 title = {Continuous Optimization of Hyper-Parameters}, |
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514 number = {1144}, |
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515 year = {1999}, |
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516 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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517 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/hyperTR.pdf}, |
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518 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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519 topics={ModelSelection},cat={T}, |
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520 } |
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521 |
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522 @INPROCEEDINGS{Bengio-icnn93, |
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523 author = {Bengio, Yoshua and Frasconi, Paolo and Simard, Patrice}, |
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524 title = {The problem of learning long-term dependencies in recurrent networks}, |
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525 booktitle = {IEEE International Conference on Neural Networks}, |
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526 year = {1993}, |
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527 pages = {1183--1195}, |
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528 publisher = {IEEE Press}, |
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529 note = {(invited paper)}, |
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530 topics={LongTerm},cat={C}, |
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531 } |
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532 |
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533 @ARTICLE{Bengio-ijprai93, |
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534 author = {Bengio, Yoshua}, |
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535 title = {A Connectionist Approach to Speech Recognition}, |
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536 journal = {International Journal on Pattern Recognition and Artificial Intelligence}, |
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537 volume = {7}, |
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538 number = {4}, |
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539 year = {1993}, |
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540 pages = {647--668}, |
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541 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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542 topics={PriorKnowledge,Speech},cat={J}, |
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543 } |
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544 |
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545 @TECHREPORT{Bengio-iohmms-TR99, |
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546 author = {Bengio, Yoshua and Lauzon, Vincent-Philippe and Ducharme, R{\'{e}}jean}, |
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547 title = {Experiments on the Application of {IOHMM}s to Model Financial Returns Series}, |
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548 number = {1146}, |
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549 year = {1999}, |
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550 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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551 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/iohmms-returnsTR.pdf}, |
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552 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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553 topics={Markov},cat={T}, |
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554 } |
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555 |
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556 @ARTICLE{bengio-lauzon-ducharme:2000, |
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557 author = {Bengio, Yoshua and Lauzon, Vincent-Philippe and Ducharme, R{\'{e}}jean}, |
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558 title = {Experiments on the Application of {IOHMM}s to Model Financial Returns Series}, |
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559 journal = {IEEE Transaction on Neural Networks}, |
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560 volume = {12}, |
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561 number = {1}, |
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562 year = {2001}, |
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563 pages = {113--123}, |
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564 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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565 topics={Markov,Finance},cat={J}, |
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566 } |
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567 |
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568 @INPROCEEDINGS{bengio-lecun-94, |
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569 author = {Bengio, Yoshua and {LeCun}, Yann}, |
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570 title = {Word normalization for on-line handwritten word recognition}, |
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571 booktitle = {Proc. of the International Conference on Pattern Recognition}, |
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572 volume = {II}, |
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573 year = {1994}, |
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574 pages = {409--413}, |
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575 publisher = {IEEE}, |
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576 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/icpr-norm.ps}, |
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577 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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578 topics={PriorKnowledge,Speech},cat={C}, |
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579 } |
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580 |
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581 @TECHREPORT{Bengio-localfailure-TR-2005, |
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582 author = {Bengio, Yoshua and Delalleau, Olivier and Le Roux, Nicolas}, |
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583 title = {The Curse of Dimensionality for Local Kernel Machines}, |
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584 number = {1258}, |
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585 year = {2005}, |
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586 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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587 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/tr1258.pdf}, |
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588 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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589 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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590 topics={HighDimensional,Kernel,Unsupervised},cat={T}, |
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591 } |
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592 |
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593 @INPROCEEDINGS{Bengio-nips-2006, |
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594 author = {Bengio, Yoshua and Lamblin, Pascal and Popovici, Dan and Larochelle, Hugo}, |
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595 title = {Greedy Layer-Wise Training of Deep Networks}, |
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596 year = {2007}, |
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597 pages = {153--160}, |
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598 crossref = {NIPS19-shorter}, |
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599 abstract = {Complexity theory of circuits strongly suggests that deep architectures can be |
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600 much more efficient (sometimes exponentially) than shallow architectures, |
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601 in terms of computational elements required to represent some functions. |
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602 Deep multi-layer neural networks have many levels of non-linearities |
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603 allowing them to compactly represent highly non-linear and |
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604 highly-varying functions. However, until recently it was not clear how |
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605 to train such deep networks, since gradient-based |
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606 optimization starting from random initialization appears to often get stuck |
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607 in poor solutions. Hinton et al. recently introduced |
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608 a greedy layer-wise unsupervised learning algorithm for Deep Belief |
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609 Networks (DBN), a generative model with many layers of hidden causal |
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610 variables. In the context of the above optimization problem, |
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611 we study this algorithm empirically and explore variants to |
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612 better understand its success and extend it to cases where the inputs are |
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613 continuous or where the structure of the input distribution is not |
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614 revealing enough about the variable to be predicted in a supervised task. |
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615 Our experiments also confirm the hypothesis that the greedy |
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616 layer-wise unsupervised training strategy mostly helps the |
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617 optimization, by initializing weights in a region near a |
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618 good local minimum, giving rise to internal distributed representations |
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619 that are high-level abstractions of the input, bringing better generalization.} |
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620 } |
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621 |
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622 @INPROCEEDINGS{Bengio-nips10, |
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623 author = {Bengio, Yoshua and Bengio, Samy and Isabelle, Jean-Fran{\c c}ois and Singer, Yoram}, |
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624 title = {Shared Context Probabilistic Transducers}, |
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625 year = {1998}, |
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626 crossref = {NIPS10-shorter}, |
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627 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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628 topics={HighDimensional},cat={C}, |
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629 } |
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630 |
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631 @TECHREPORT{Bengio-NLMP-TR-2005, |
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632 author = {Bengio, Yoshua and Larochelle, Hugo}, |
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633 title = {Non-Local Manifold Parzen Windows}, |
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634 number = {1264}, |
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635 year = {2005}, |
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636 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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637 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/NLMP-techreport.pdf}, |
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638 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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639 topics={HighDimensional,Kernel,Unsupervised},cat={T}, |
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640 } |
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641 |
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642 @INPROCEEDINGS{Bengio-nncm96, |
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643 author = {Bengio, Yoshua}, |
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644 editor = {Weigend, A.S. and Abu-Mostafa, Y.S. and Refenes, A. -P. N.}, |
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645 title = {Training A Neural Network with a Financial Criterion Rather than a Prediction Criterion}, |
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646 booktitle = {Proceedings of the Fourth International Conference on Neural Networks in the Capital Markets ({NNCM}-96)}, |
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647 year = {1997}, |
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648 pages = {433--443}, |
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649 publisher = {World Scientific}, |
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650 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/nncm.pdf}, |
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651 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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652 topics={Finance,PriorKnowledge,Discriminant},cat={C}, |
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653 } |
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654 |
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655 @TECHREPORT{Bengio-NonStat-Hyper-TR, |
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656 author = {Bengio, Yoshua and Dugas, Charles}, |
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657 title = {Learning Simple Non-Stationarities with Hyper-Parameters}, |
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658 number = {1145}, |
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659 year = {1999}, |
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660 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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661 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/nonstatTR.pdf}, |
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662 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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663 topics={ModelSelection,Finance},cat={T}, |
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664 } |
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665 |
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666 @ARTICLE{Bengio-scholarpedia-2007, |
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667 author = {Bengio, Yoshua}, |
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668 title = {Neural net language models}, |
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669 journal = {Scholarpedia}, |
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670 volume = {3}, |
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671 number = {1}, |
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672 year = {2008}, |
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673 pages = {3881}, |
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674 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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675 |
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676 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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677 |
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678 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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679 |
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680 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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681 |
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682 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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683 |
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684 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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685 } |
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686 |
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687 @TECHREPORT{Bengio-TR1312, |
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688 author = {Bengio, Yoshua}, |
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689 title = {Learning deep architectures for AI}, |
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690 number = {1312}, |
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691 year = {2007}, |
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692 institution = {Dept. IRO, Universite de Montreal}, |
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693 note = {Preliminary version of journal article with the same title appearing in Foundations and Trends in Machine Learning (2009)}, |
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694 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/TR1312.pdf}, |
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695 abstract = {Theoretical results strongly suggest that in order to learn the kind of |
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696 complicated functions that can represent high-level abstractions (e.g. in |
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697 vision, language, and other AI-level tasks), one may need deep |
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698 architectures. Deep architectures are composed of multiple levels of non-linear |
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699 operations, such as in neural nets with many hidden layers. Searching the |
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700 parameter space of deep architectures is a difficult optimization task, but |
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701 learning algorithms such as those for Deep Belief Networks have recently been proposed |
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702 to tackle this problem with notable success, beating the state-of-the-art |
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703 in certain areas. This paper discusses the motivations and principles regarding |
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704 learning algorithms for deep architectures and in particular for those based |
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705 on unsupervised learning such as Deep Belief Networks, using as building |
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706 blocks single-layer models such as Restricted {Boltzmann} Machines.} |
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707 } |
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708 |
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709 @ARTICLE{Bengio-trnn94, |
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710 author = {Bengio, Yoshua and Simard, Patrice and Frasconi, Paolo}, |
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711 title = {Learning Long-Term Dependencies with Gradient Descent is Difficult}, |
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712 journal = {IEEE Transactions on Neural Networks}, |
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713 volume = {5}, |
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714 number = {2}, |
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715 year = {1994}, |
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716 pages = {157--166}, |
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717 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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718 optnote={(Special Issue on Recurrent Neural Networks)},topics={LongTerm},cat={J}, |
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719 } |
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720 |
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721 @INPROCEEDINGS{Bengio-wirn93, |
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722 author = {Bengio, Yoshua and Frasconi, Paolo and Gori, Marco and Soda, G.}, |
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723 editor = {Caianello, E.}, |
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724 title = {Recurrent Neural Networks for Adaptive Temporal Processing}, |
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725 booktitle = {Proc. of the 6th Italian Workshop on Neural Networks, WIRN-93}, |
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726 year = {1993}, |
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727 pages = {1183--1195}, |
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728 publisher = {World Scientific Publ.}, |
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729 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/rnn_review93.ps}, |
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730 topics={LongTerm},cat={C}, |
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731 } |
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732 |
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733 @ARTICLE{Bengio2000c, |
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734 author = {Bengio, Yoshua}, |
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735 title = {Gradient-Based Optimization of Hyperparameters}, |
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736 journal = {Neural Computation}, |
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737 volume = {12}, |
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738 number = {8}, |
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739 year = {2000}, |
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740 pages = {1889--1900}, |
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741 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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742 topics={ModelSelection},cat={J}, |
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743 } |
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744 |
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745 @ARTICLE{Bengio89a, |
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746 author = {Bengio, Yoshua and Cardin, Regis and De Mori, Renato and Merlo, Ettore}, |
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747 title = {Programmable execution of multi-layered networks for automatic speech recognition}, |
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748 journal = {Communications of the Association for Computing Machinery}, |
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749 volume = {32}, |
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750 number = {2}, |
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751 year = {1989}, |
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752 pages = {195--199}, |
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753 topics={Speech},cat={J}, |
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754 } |
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755 |
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756 @INPROCEEDINGS{Bengio89c, |
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757 author = {Bengio, Yoshua and Cosi, Piero and Cardin, Regis and De Mori, Renato}, |
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758 title = {Use of multi-layered networks for coding speech with phonetic features}, |
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759 year = {1989}, |
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760 pages = {224--231}, |
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761 crossref = {NIPS1-shorter}, |
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762 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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763 topics={Speech},cat={C}, |
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764 } |
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765 |
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766 @INPROCEEDINGS{Bengio89d, |
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767 author = {De Mori, Renato and Bengio, Yoshua and Cosi, Piero}, |
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768 title = {On the generalization capability of multilayered networks in the extraction of speech properties}, |
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769 booktitle = {Proceedings of the International Joint Conference on Artificial Intelligence}, |
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770 year = {1989}, |
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771 pages = {1531--1536}, |
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772 publisher = {IEEE}, |
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773 topics={Speech},cat={C}, |
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774 } |
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775 |
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776 @INPROCEEDINGS{Bengio90, |
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777 author = {Bengio, Yoshua and Cardin, Regis and De Mori, Renato}, |
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778 title = {Speaker Independent Speech Recognition with Neural Networks and Speech Knowledge}, |
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779 year = {1990}, |
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780 pages = {218--225}, |
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781 crossref = {NIPS2-shorter}, |
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782 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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783 topics={PriorKnowledge,Speech},cat={C}, |
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784 } |
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785 |
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786 @INCOLLECTION{Bengio90b, |
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787 author = {Bengio, Yoshua}, |
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788 title = {Radial Basis Functions for speech recognition}, |
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789 booktitle = {Speech Recognition and Understanding: Recent Advances, Trends and Applications}, |
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790 year = {1990}, |
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791 pages = {293--298}, |
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792 publisher = {NATO Advanced Study Institute Series F: Computer and Systems Sciences}, |
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793 topics={Kernel,Speech},cat={B}, |
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794 } |
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795 |
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796 @INCOLLECTION{Bengio90c, |
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797 author = {Bengio, Yoshua and De Mori, Renato}, |
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798 editor = {{Fogelman Soulie}, F. and Herault, J.}, |
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799 title = {Speech coding with multilayer networks}, |
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800 booktitle = {Neurocomputing: Algorithms, Architectures and Applications}, |
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801 year = {1990}, |
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|
802 pages = {207--216}, |
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803 publisher = {NATO Advanced Study Institute Series F: Computer and Systems Sciences}, |
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804 topics={Speech},cat={B}, |
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805 } |
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806 |
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807 @INPROCEEDINGS{Bengio90e, |
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808 author = {Bengio, Yoshua and Pouliot, Yannick and Bengio, Samy and Agin, Patrick}, |
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809 title = {A neural network to detect homologies in proteins}, |
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810 year = {1990}, |
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811 pages = {423--430}, |
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812 crossref = {NIPS2-shorter}, |
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813 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.}, |
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814 topics={Bioinformatic,PriorKnowledge},cat={C}, |
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815 } |
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816 |
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817 @INPROCEEDINGS{Bengio90z, |
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818 author = {Bengio, Yoshua and De Mori, Renato and Gori, Marco}, |
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819 editor = {Caianello, E.}, |
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820 title = {Experiments on automatic speech recognition using BPS}, |
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821 booktitle = {Parallel Architectures and Neural Networks}, |
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822 year = {1990}, |
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823 pages = {223--232}, |
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824 publisher = {World Scientific Publ.}, |
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825 topics={Speech},cat={C}, |
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826 } |
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827 |
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828 @INPROCEEDINGS{Bengio91a, |
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829 author = {Bengio, Yoshua and De Mori, Renato and Flammia, Giovanni and Kompe, Ralf}, |
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830 title = {A comparative study of hybrid acoustic phonetic decoders based on artificial neural networks}, |
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831 booktitle = {Proceedings of EuroSpeech'91}, |
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832 year = {1991}, |
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833 topics={PriorKnowledge,Speech},cat={C}, |
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834 } |
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835 |
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836 @INPROCEEDINGS{Bengio91b, |
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837 author = {Bengio, Yoshua and De Mori, Renato and Flammia, Giovanni and Kompe, Ralf}, |
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838 title = {Global Optimization of a Neural Network - Hidden {M}arkov Model Hybrid}, |
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839 booktitle = {Proceedings of EuroSpeech'91}, |
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840 year = {1991}, |
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841 topics={Markov},cat={C}, |
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842 } |
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843 |
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844 @INPROCEEDINGS{Bengio91z, |
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845 author = {Bengio, Yoshua and De Mori, Renato and Flammia, Giovanni and Kompe, Ralf}, |
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846 title = {Phonetically motivated acoustic parameters for continuous speech recognition using artificial neural networks}, |
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847 booktitle = {Proceedings of EuroSpeech'91}, |
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848 year = {1991}, |
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849 cat={C}, |
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850 } |
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851 |
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852 @ARTICLE{Bengio92b, |
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853 author = {Bengio, Yoshua and De Mori, Renato and Flammia, Giovanni and Kompe, Ralf}, |
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854 title = {Phonetically motivated acoustic parameters for continuous speech recognition using artificial neural networks}, |
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855 journal = {Speech Communication}, |
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856 volume = {11}, |
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857 number = {2--3}, |
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858 year = {1992}, |
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859 pages = {261--271}, |
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860 note = {Special issue on neurospeech}, |
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861 topics={PriorKnowledge,Speech},cat={J}, |
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862 } |
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863 |
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864 @INPROCEEDINGS{Bengio92c, |
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865 author = {Bengio, Yoshua and De Mori, Renato and Flammia, Giovanni and Kompe, Ralf}, |
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866 title = {Neural Network - Gaussian Mixture Hybrid for Speech Recognition or Density Estimation}, |
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867 year = {1992}, |
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868 pages = {175--182}, |
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869 crossref = {NIPS4-shorter}, |
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870 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.}, |
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871 topics={Speech},cat={C}, |
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872 } |
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873 |
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874 @INPROCEEDINGS{Bengio94d, |
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875 author = {Frasconi, Paolo and Bengio, Yoshua}, |
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876 title = {An {EM} Approach to Grammatical Inference: Input/Output {HMMs}}, |
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877 booktitle = {International Conference on Pattern Recognition (ICPR'94)}, |
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878 year = {1994}, |
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879 pages = {289--294}, |
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880 topics={Markov,LongTerm},cat={C}, |
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881 } |
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882 |
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883 @ARTICLE{Bengio96, |
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884 author = {Bengio, Yoshua and Frasconi, Paolo}, |
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885 title = {Input/{O}utput {HMM}s for Sequence Processing}, |
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886 journal = {IEEE Transactions on Neural Networks}, |
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887 volume = {7}, |
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888 number = {5}, |
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889 year = {1996}, |
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890 pages = {1231--1249}, |
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891 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. |
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892 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. |
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893 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.}, |
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894 topics={Markov},cat={J}, |
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895 } |
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896 |
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897 @TECHREPORT{Bengio96-hmmsTR, |
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898 author = {Bengio, Yoshua}, |
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899 title = {Markovian Models for Sequential Data}, |
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900 number = {1049}, |
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901 year = {1996}, |
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902 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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903 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/hmmsTR.pdf}, |
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904 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.}, |
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905 topics={Markov},cat={T}, |
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906 } |
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907 |
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908 @ARTICLE{Bengio97, |
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909 author = {Bengio, Yoshua}, |
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910 title = {Using a Financial Training Criterion Rather than a Prediction Criterion}, |
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911 journal = {International Journal of Neural Systems}, |
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912 volume = {8}, |
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913 number = {4}, |
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914 year = {1997}, |
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915 pages = {433--443}, |
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916 note = {Special issue on noisy time-series}, |
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917 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.}, |
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918 topics={Finance,PriorKnowledge,Discriminant},cat={J}, |
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919 } |
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920 |
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921 @ARTICLE{Bengio99a, |
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922 author = {Bengio, Yoshua}, |
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923 title = {Markovian Models for Sequential Data}, |
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924 journal = {Neural Computing Surveys}, |
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925 volume = {2}, |
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926 year = {1999}, |
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927 pages = {129--162}, |
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928 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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929 topics={Markov},cat={J}, |
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930 } |
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931 |
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932 @ARTICLE{Bengio99b, |
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933 author = {Bengio, Samy and Bengio, Yoshua and Robert, Jacques and B{\'{e}}langer, Gilles}, |
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934 title = {Stochastic Learning of Strategic Equilibria for Auctions}, |
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935 journal = {Neural Computation}, |
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936 volume = {11}, |
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937 number = {5}, |
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938 year = {1999}, |
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939 pages = {1199--1209}, |
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940 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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941 topics={Auction},cat={J}, |
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942 } |
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943 |
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944 @TECHREPORT{bengio:1990, |
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945 author = {Bengio, Yoshua}, |
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946 title = {Learning a Synaptic Learning Rule}, |
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947 number = {751}, |
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948 year = {1990}, |
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949 institution = {D{\'{e}}partement d'Informatique et de Recherche Op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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950 topics={BioRules},cat={T}, |
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951 } |
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952 |
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953 @INPROCEEDINGS{bengio:1990:snowbird, |
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954 author = {Bengio, Yoshua and R., De Mori}, |
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955 title = {Recurrent networks with Radial Basis Functions for speech recognition}, |
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956 booktitle = {1990 Neural Networks for Computing Conference}, |
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957 year = {1990}, |
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958 topics={Speech},cat={C}, |
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959 } |
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960 |
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961 @INPROCEEDINGS{bengio:1991:ijcnn, |
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962 author = {Bengio, Yoshua and Bengio, Samy and Cloutier, Jocelyn}, |
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963 title = {Learning a Synaptic Learning Rule}, |
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964 booktitle = {Proceedings of the International Joint Conference on Neural Networks}, |
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965 year = {1991}, |
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966 pages = {II--A969}, |
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967 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/bengio_1991_ijcnn.ps}, |
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968 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.}, |
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969 addressfr={Seattle, USA},topics={BioRules},cat={C}, |
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970 } |
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971 |
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972 @INPROCEEDINGS{bengio:1991:nnc, |
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973 author = {Bengio, Yoshua and Bengio, Samy and Cloutier, Jocelyn}, |
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974 title = {Learning Synaptic Learning Rules}, |
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975 booktitle = {Neural Networks for Computing}, |
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976 year = {1991}, |
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977 addressfr={Snowbird, Utah, USA},topics={BioRules},cat={C}, |
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978 } |
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979 |
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980 @INPROCEEDINGS{bengio:1991:snowbird, |
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981 author = {Bengio, Yoshua and Bengio, Samy and Cloutier, Jocelyn}, |
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982 title = {Learning a Synaptic Learning Rule}, |
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983 booktitle = {1991 Neural Networks for Computing Conference}, |
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984 year = {1991}, |
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985 topics={BioRules},cat={C}, |
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986 } |
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987 |
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988 @INPROCEEDINGS{bengio:1992:nn, |
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989 author = {Bengio, Samy and Bengio, Yoshua and Cloutier, Jocelyn and Gecsei, Jan}, |
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990 title = {Aspects th{\'{e}}oriques de l'optimisation d'une r{\`{e}}gle d'apprentissage}, |
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991 booktitle = {Actes de la conf{\'{e}}rence Neuro-N{\^{\i}}mes 1992}, |
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992 year = {1992}, |
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993 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/bengio_1992_nn.ps}, |
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994 abstract = {Ayant expos{\'{e}} dans de pr{\'{e}}c{\'{e}}dentes publications (voir [Beng90, Beng92] notamment) lid{\'{e}}e que lon pouvait optimiser des r{\`{e}}gles dapprentissage 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}} loptimisation dune r{\`{e}}gle dapprentissage 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 dune r{\`{e}}gle dapprentissage dont les param{\`{e}}tres ont {\'{e}}t{\'{e}} estim{\'{e}}s {\`{a}} partir dun certain ensemble de t{\^{a}}ches. Enfin, nous exposons bri{\`{e}}vement les r{\'{e}}sultats dune exp{\'{e}}rience consistant {\`{a}} trouver, par descente du gradient, une r{\`{e}}gle dapprentissage pouvant r{\'{e}}soudre plusieurs t{\^{a}}ches bool{\'{e}}ennes lin{\'{e}}airement et non lin{\'{e}}airement s{\'{e}}parables.}, |
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995 addressfr={N{\^i}es, France},topics={BioRules},cat={C}, |
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996 } |
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997 |
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998 @INPROCEEDINGS{bengio:1992:oban, |
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999 author = {Bengio, Samy and Bengio, Yoshua and Cloutier, Jocelyn and Gecsei, Jan}, |
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1000 title = {On the Optimization of a Synaptic Learning rule}, |
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1001 booktitle = {Conference on Optimality in Biological and Artificial Networks}, |
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1002 year = {1992}, |
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1003 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/bengio_1992_oban.ps}, |
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1004 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.}, |
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1005 addressfr={Dallas, USA},topics={BioRules},cat={C}, |
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1006 } |
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1007 |
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1008 @INPROCEEDINGS{bengio:1992:snowbird, |
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1009 author = {Bengio, Yoshua}, |
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1010 title = {Representations Based on Articulatory Dynamics for Speech Recognition}, |
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1011 booktitle = {1992 Neural Networks for Computing Conference}, |
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1012 year = {1992}, |
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1013 topics={PriorKnowledge,Speech},cat={C}, |
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1014 } |
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1015 |
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1016 @INPROCEEDINGS{bengio:1993:icann, |
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1017 author = {Bengio, Samy and Bengio, Yoshua and Cloutier, Jocelyn and Gecsei, Jan}, |
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1018 editor = {Gielen, S. and Kappen, B.}, |
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1019 title = {Generalization of a Parametric Learning Rule}, |
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1020 booktitle = {{ICANN} '93: Proceedings of the International Conference on Artificial Neural Networks}, |
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1021 year = {1993}, |
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1022 pages = {502}, |
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1023 publisher = {Springer-Verlag}, |
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1024 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/bengio_1993_icann.ps}, |
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1025 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.}, |
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1026 addressfr={Amsterdam, Pays-Bas},topics={BioRules},cat={C}, |
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1027 } |
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1028 |
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1029 @INPROCEEDINGS{bengio:1993:snowbird, |
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1030 author = {Bengio, Yoshua and Simard, Patrice and Frasconi, Paolo}, |
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1031 title = {The Problem of Learning Long-Term Dependencies in Recurrent Networks}, |
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1032 booktitle = {1993 Neural Networks for Computing Conference}, |
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1033 year = {1993}, |
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1034 topics={LongTerm},cat={C}, |
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1035 } |
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1036 |
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1037 @TECHREPORT{bengio:1994, |
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1038 author = {Bengio, Yoshua and Frasconi, Paolo}, |
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1039 title = {An {EM} Approach to Learning Sequential Behavior}, |
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1040 number = {DSI 11-94}, |
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1041 year = {1994}, |
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1042 institution = {Universita di Firenze, Dipartimento di Sistemi e Informatica}, |
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1043 topics={LongTerm},cat={T}, |
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1044 } |
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1045 |
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1046 @INPROCEEDINGS{bengio:1994:acfas, |
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1047 author = {Bengio, Samy and Bengio, Yoshua and Cloutier, Jocelyn and Gecsei, Jan}, |
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1048 title = {Optimisation d'une r{\`{e}}gle d'apprentissage pour r{\'{e}}seaux de neurones artificiels}, |
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1049 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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1050 year = {1994}, |
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1051 topics={BioRules},cat={C}, |
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1052 } |
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1053 |
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1054 @INPROCEEDINGS{bengio:1994:snowbird, |
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1055 author = {Bengio, Yoshua and Frasconi, Paolo}, |
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1056 title = {An {EM} Algorithm for Target Propagation}, |
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1057 booktitle = {1994 Neural Networks for Computing Conference}, |
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1058 year = {1994}, |
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1059 topics={LongTerm},cat={C}, |
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1060 } |
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1061 |
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1062 @INPROCEEDINGS{bengio:1994:wcci, |
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1063 author = {Bengio, Samy and Bengio, Yoshua and Cloutier, Jocelyn}, |
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1064 title = {Use of Genetic Programming for the Search of a New Learning Rule for Neural Networks}, |
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1065 booktitle = {Proceedings of the First Conference on Evolutionary Computation, {IEEE} World Congress on Computational Intelligence}, |
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1066 year = {1994}, |
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1067 pages = {324--327}, |
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1068 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/bengio_1994_wcci.ps}, |
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1069 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.}, |
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1070 topics={BioRules},cat={C}, |
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1071 } |
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1072 |
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1073 @INPROCEEDINGS{bengio:1994b:acfas, |
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1074 author = {Bengio, Yoshua and Frasconi, Paolo}, |
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1075 title = {R{\'{e}}seaux de neurones {M}arkoviens pour l'inf{\'{e}}rence grammaticale}, |
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1076 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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1077 year = {1994}, |
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1078 topics={Markov,Language},cat={C}, |
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1079 } |
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1080 |
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1081 @ARTICLE{bengio:1995:npl, |
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1082 author = {Bengio, Samy and Bengio, Yoshua and Cloutier, Jocelyn}, |
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1083 title = {On the Search for New Learning Rules for {ANN}s}, |
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1084 journal = {Neural Processing Letters}, |
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1085 volume = {2}, |
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1086 number = {4}, |
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1087 year = {1995}, |
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1088 pages = {26--30}, |
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1089 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.}, |
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1090 topics={BioRules},cat={J}, |
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1091 } |
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1092 |
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1093 @INCOLLECTION{bengio:1995:oban, |
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1094 author = {Bengio, Samy and Bengio, Yoshua and Cloutier, Jocelyn and Gecsei, Jan}, |
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1095 editor = {Levine, D. S. and Elsberry, W. R.}, |
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1096 title = {{O}n the Optimization of a Synaptic Learning Rule}, |
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1097 booktitle = {Optimality in Biological and Artificial Networks}, |
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1098 year = {1995}, |
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1099 publisher = {Lawrence Erlbaum}, |
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1100 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/bengio_1995_oban.pdf}, |
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1101 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.}, |
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1102 topics={BioRules},cat={B}, |
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1103 } |
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1104 |
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1105 @TECHREPORT{bengio:1996:udem, |
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1106 author = {Bengio, Yoshua and Bengio, Samy}, |
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1107 title = {Training Asynchronous Input/Output Hidden {M}arkov Models}, |
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1108 number = {1013}, |
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1109 year = {1996}, |
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1110 institution = {D{\'{e}}partement d'Informatique et de Recherche Op{\'{e}}rationnelle, Universit{\'{e}}de Montr{\'{e}}al}, |
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1111 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/bengio_1996_udem.ps}, |
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1112 topics={Markov},cat={T}, |
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1113 } |
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1114 |
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1115 @INPROCEEDINGS{bengio:1997:snowbird, |
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1116 author = {Bengio, Yoshua and Bengio, Samy and Singer, Yoram and Isabelle, Jean-Fran{\c c}ois}, |
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1117 title = {On the Clusterization of Probabilistic Transducers}, |
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1118 booktitle = {1997 Neural Networks for Computing Conference}, |
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1119 year = {1997}, |
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1120 topics={HighDimensional},cat={C}, |
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1121 } |
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1122 |
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1123 @INPROCEEDINGS{bengio:1998:snowbird, |
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1124 author = {Bengio, Samy and Bengio, Yoshua and Robert, Jacques and B{\'{e}}langer, Gilles}, |
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1125 title = {Stochastic Learning of Strategic Equilibria for Auctions}, |
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1126 booktitle = {Learning Conference}, |
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1127 year = {1998}, |
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1128 topics={Auction},cat={C}, |
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1129 } |
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1130 |
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1131 @TECHREPORT{bengio:1998:udem, |
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1132 author = {Bengio, Samy and Bengio, Yoshua and Robert, Jacques and B{\'{e}}langer, Gilles}, |
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1133 title = {Stochastic Learning of Strategic Equilibria for Auctions}, |
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1134 number = {1119}, |
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1135 year = {1998}, |
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1136 institution = {D{\'{e}}partement d'Informatique et de Recherche Op{\'{e}}rationnelle, Universit{\'{e}}de Montr{\'{e}}al}, |
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1137 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/bengio_1998_udem.pdf}, |
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1138 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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1139 topics={Auction},cat={T}, |
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1140 } |
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1141 |
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1142 @INPROCEEDINGS{bengio:1999:snowbird, |
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1143 author = {Bengio, Yoshua and Latendresse, Simon and Dugas, Charles}, |
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1144 title = {Gradient-Based Learning of Hyper-Parameters}, |
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1145 booktitle = {Learning Conference}, |
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1146 year = {1999}, |
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1147 topics={ModelSelection},cat={C}, |
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1148 } |
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1149 |
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1150 @INPROCEEDINGS{bengio:1999:titration, |
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1151 author = {Bengio, Yoshua and Brault, J-J. and Major, Fran{\c c}ois and Neal, R. and Pigeon, Steven}, |
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1152 title = {Learning Algorithms for Sorting Compounds from Titration Curves}, |
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1153 booktitle = {Symposium on New Perspectives for Computer-Aided Drug Design}, |
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1154 year = {1999}, |
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1155 topics={Speech},cat={C}, |
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1156 } |
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1157 |
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1158 @ARTICLE{bengio:2000:ieeetrnn, |
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1159 author = {Bengio, Samy and Bengio, Yoshua}, |
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1160 title = {Taking on the Curse of Dimensionality in Joint Distributions Using Neural Networks}, |
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1161 journal = {IEEE Transaction on Neural Networks special issue on data mining and knowledge discovery}, |
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1162 volume = {11}, |
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1163 number = {3}, |
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1164 year = {2000}, |
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1165 pages = {550--557}, |
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1166 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.}, |
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1167 topics={HighDimensional,Unsupervised,Mining},cat={J}, |
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1168 } |
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1169 |
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1170 @INPROCEEDINGS{bengio:2000:nips, |
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1171 author = {Bengio, Yoshua and Bengio, Samy}, |
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1172 title = {Modeling High-Dimensional Discrete Data with Multi-Layer Neural Networks}, |
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1173 year = {2000}, |
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1174 pages = {400--406}, |
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1175 crossref = {NIPS12-shorter}, |
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1176 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.}, |
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1177 topics={HighDimensional,Unsupervised},cat={C}, |
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1178 } |
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1179 |
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1180 @ARTICLE{bengio:2003, |
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1181 author = {Bengio, Yoshua and Ducharme, R{\'{e}}jean and Vincent, Pascal and Jauvin, Christian}, |
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1182 title = {A Neural Probabilistic Language Model}, |
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1183 volume = {3}, |
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1184 year = {2003}, |
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1185 pages = {1137--1155}, |
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583
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1186 crossref = {JMLR-shorter}, |
498
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1187 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.}, |
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1188 topics={Markov,Unsupervised,Language},cat={J}, |
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1189 } |
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1190 |
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1191 @TECHREPORT{bengio:socs-1990, |
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1192 author = {Bengio, Yoshua and De Mori, Renato and Flammia, Giovanni and Kompe, Ralf}, |
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1193 title = {Global Optimization of a Neural Network - Hidden {M}arkov Model Hybrid}, |
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1194 number = {TR-SOCS-90.22}, |
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1195 year = {1990}, |
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1196 institution = {School of Computer Science, McGill University}, |
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1197 topics={Markov},cat={T}, |
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1198 } |
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1199 |
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1200 @INPROCEEDINGS{bengioc:1994:acfas, |
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1201 author = {Bengio, Yoshua and {LeCun}, Yann}, |
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1202 title = {Reconnaissance de mots manuscrits avec r{\'{e}}seaux de neurones et mod{\`{e}}les de {M}arkov}, |
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1203 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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1204 year = {1994}, |
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1205 topics={Markov,Speech},cat={C}, |
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1206 } |
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1207 |
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1208 @TECHREPORT{Bengio_Bottou92, |
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1209 author = {Bengio, Yoshua and Bottou, {L{\'{e}}on}}, |
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1210 title = {A New Approach to Estimating Probability Density Functions with Artificial Neural Networks}, |
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1211 number = {TR-92.02}, |
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1212 year = {1992}, |
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1213 institution = {Massachusetts Institute of Technology, Dept. Brain and Cognitive Sciences}, |
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1214 topics={HighDimensional},cat={T}, |
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1215 } |
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1216 |
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1217 @INCOLLECTION{bengio_extension_nips_2003, |
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1218 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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1219 keywords = {dimensionality reduction, eigenfunctions learning, Isomap, kernel {PCA}, locally linear embedding, Nystrom formula, spectral methods}, |
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1220 title = {Out-of-Sample Extensions for {LLE}, Isomap, {MDS}, Eigenmaps, and Spectral Clustering}, |
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1221 year = {2004}, |
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1222 crossref = {NIPS16-shorter}, |
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1223 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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1224 topics={HighDimensional,Kernel,Unsupervised},cat={C}, |
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1225 } |
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1226 |
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1227 @ARTICLE{Bengio_Gingras98a, |
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1228 author = {Bengio, Yoshua and Gingras, Fran{\c c}ois and Goulard, Bernard and Lina, Jean-Marc}, |
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1229 title = {Gaussian Mixture Densities for Classification of Nuclear Power Plant Data}, |
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1230 journal = {Computers and Artificial Intelligence}, |
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1231 volume = {17}, |
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1232 number = {2-3}, |
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1233 year = {1998}, |
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1234 pages = {189--209}, |
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1235 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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1236 topics={Mining},cat={J}, |
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1237 } |
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1238 |
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1239 @ARTICLE{Bengio_Gingras98b, |
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1240 author = {Gingras, Fran{\c c}ois and Bengio, Yoshua}, |
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1241 title = {Handling Asynchronous or Missing Financial Data with Recurrent Networks}, |
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1242 journal = {International Journal of Computational Intelligence and Organizations}, |
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1243 volume = {1}, |
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1244 number = {3}, |
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1245 year = {1998}, |
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1246 pages = {154--163}, |
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1247 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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1248 topics={Finance,Missing},cat={J}, |
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1249 } |
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1250 |
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1251 @INPROCEEDINGS{Bengio_icassp90, |
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1252 author = {Bengio, Yoshua and Cardin, Regis and De Mori, Renato and Normandin, Yves}, |
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1253 title = {A Hybrid Coder for Hidden {M}arkov Models Using a Recurrent Neural Network}, |
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1254 booktitle = {International Conference on Acoustics, Speech and Signal Processing}, |
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1255 year = {1990}, |
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1256 pages = {537--540}, |
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1257 topics={Markov,Speech},cat={C}, |
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1258 } |
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1259 |
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1260 @INPROCEEDINGS{Bengio_LeCun94, |
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1261 author = {Bengio, Yoshua and {LeCun}, Yann and Henderson, Donnie}, |
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1262 title = {Globally Trained Handwritten Word Recognizer using Spatial Representation, Space Displacement Neural Networks and Hidden {M}arkov Models}, |
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1263 year = {1994}, |
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1264 pages = {937--944}, |
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1265 crossref = {NIPS6-shorter}, |
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1266 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.}, |
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1267 topics={Speech},cat={C}, |
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1268 } |
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1269 |
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1270 @ARTICLE{Bengio_LeCun95, |
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1271 author = {Bengio, Yoshua and {LeCun}, Yann and Nohl, Craig and Burges, Chris}, |
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1272 title = {LeRec: A {NN}/{HMM} Hybrid for On-Line Handwriting Recognition}, |
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1273 journal = {Neural Computation}, |
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1274 volume = {7}, |
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1275 number = {6}, |
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1276 year = {1995}, |
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1277 pages = {1289--1303}, |
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1278 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.}, |
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1279 topics={PriorKnowledge,Speech},cat={J}, |
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1280 } |
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1281 |
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1282 @ARTICLE{Bengio_prel92, |
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1283 author = {Bengio, Yoshua and Gori, Marco and De Mori, Renato}, |
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1284 title = {Learning the Dynamic Nature of Speech with Back-propagation for Sequences}, |
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1285 journal = {Pattern Recognition Letters}, |
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1286 volume = {13}, |
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1287 number = {5}, |
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1288 year = {1992}, |
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1289 pages = {375--385}, |
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1290 note = {(Special issue on Artificial Neural Networks)}, |
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1291 topics={Speech},cat={J}, |
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1292 } |
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1293 |
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1294 @ARTICLE{Bengio_trnn92, |
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1295 author = {Bengio, Yoshua and De Mori, Renato and Flammia, Giovanni and Kompe, Ralf}, |
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1296 title = {Global Optimization of a Neural Network-Hidden {M}arkov Model Hybrid}, |
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1297 journal = {IEEE Transactions on Neural Networks}, |
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1298 volume = {3}, |
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1299 number = {2}, |
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1300 year = {1992}, |
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1301 pages = {252--259}, |
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1302 topics={Markov},cat={J}, |
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1303 } |
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1304 |
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1305 @TECHREPORT{Bergstra+2009, |
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1306 author = {Bergstra, James and Desjardins, Guillaume and Lamblin, Pascal and Bengio, Yoshua}, |
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1307 title = {Quadratic Polynomials Learn Better Image Features}, |
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1308 number = {1337}, |
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1309 year = {2009}, |
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1310 institution = {D{\'{e}}partement d'Informatique et de Recherche Op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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1311 abstract = {The affine-sigmoidal hidden unit (of the form $\sigma(ax+b)$) |
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1312 is a crude predictor of neuron response in visual area V1. |
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1313 More descriptive models of V1 have been advanced that are no more computationally expensive, |
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1314 yet artificial neural network research continues to focus on networks of affine-sigmoidal models. |
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1315 This paper identifies two qualitative differences between the affine-sigmoidal hidden unit |
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1316 and a particular recent model of V1 response: |
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1317 a) the presence of a low-rank quadratic term in the argument to $\sigma$, |
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1318 and b) the use of a gentler non-linearity than the $\tanh$ or logistic sigmoid. |
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1319 We evaluate these model ingredients by training single-layer |
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1320 neural networks to solve three image classification tasks. |
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1321 We experimented with fully-connected hidden units, |
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1322 as well as locally-connected units and convolutional units |
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1323 that more closely mimic the function and connectivity of the visual system. |
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1324 On all three tasks, both the quadratic interactions and the gentler non-linearity |
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1325 lead to significantly better generalization. |
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1326 The advantage of quadratic units was strongest in conjunction with sparse and convolutional hidden units.} |
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1327 } |
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1328 |
604
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1329 @ARTICLE{Bergstra+al-2010, |
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1330 author = {Bergstra, James and Bengio, Yoshua and Louradour, Jerome}, |
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1331 title = {Suitability of V1 Energy Models for Object Classification}, |
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1332 journal = {Neural Computation}, |
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1333 year = {2010}, |
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1334 note = {to appear} |
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1335 } |
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1336 |
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1337 @INPROCEEDINGS{bergstra+al:2010-scipy, |
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1338 author = {Bergstra, James and Breuleux, Olivier and Bastien, Fr{\'{e}}d{\'{e}}ric and Lamblin, Pascal and Pascanu, Razvan and Desjardins, Guillaume and Turian, Joseph and Bengio, Yoshua}, |
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1339 title = {Theano: a {CPU} and {GPU} Math Expression Compiler}, |
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1340 booktitle = {Proceedings of the Python for Scientific Computing Conference ({SciPy})}, |
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1341 year = {2010}, |
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1342 note = {Oral} |
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1343 } |
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1344 |
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1345 @MISC{bergstra+al:2010-sharcnet, |
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1346 author = {Bergstra, James and Bengio, Yoshua}, |
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1347 title = {{GPU} Programming with Theano}, |
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1348 year = {2010}, |
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1349 howpublished = {{SHARCNET} Research Day}, |
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1350 note = {Oral} |
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1351 } |
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1352 |
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1353 @MISC{bergstra+al:2010snowbird, |
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1354 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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1355 title = {Deep Learning on {GPU}s with Theano}, |
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1356 booktitle = {The Learning Workshop}, |
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1357 year = {2010}, |
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1358 note = {Oral} |
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1359 } |
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1360 |
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1361 @INPROCEEDINGS{Bergstra+Bengio-2009, |
604
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1362 author = {Bergstra, James and Bengio, Yoshua}, |
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1363 title = {Slow, Decorrelated Features for Pretraining Complex Cell-like Networks}, |
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1364 year = {2009}, |
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1365 pages = {99--107}, |
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1366 publisher = {MIT Press}, |
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1367 url = {http://books.nips.cc/papers/files/nips22/NIPS2009_0933.pdf}, |
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1368 crossref = {NIPS22} |
498
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1369 } |
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1370 |
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1371 @ARTICLE{bergstra+casagrande+erhan+eck+kegl:2006, |
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1372 author = {Bergstra, James and Casagrande, Norman and Erhan, Dumitru and Eck, Douglas and K{\'{e}}gl, Bal{\'{a}}zs}, |
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1373 title = {Aggregate Features and AdaBoost for Music Classification}, |
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1374 journal = {Machine Learning}, |
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1375 volume = {65}, |
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1376 year = {2006}, |
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1377 pages = {473--484}, |
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1378 issn = {0885-6125}, |
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1379 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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1380 PDF = {papers/2006_ml_draft.pdf}, |
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1381 SOURCE = {OwnPublication}, |
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1382 } |
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1383 |
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1384 @INPROCEEDINGS{bergstra+lacoste+eck:2006, |
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1385 author = {Bergstra, James and Lacoste, Alexandre and Eck, Douglas}, |
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1386 title = {Predicting Genre Labels for Artists using FreeDB}, |
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1387 booktitle = {Proc. 7th International Conference on Music Information Retrieval ({ISMIR})}, |
498
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1388 year = {2006}, |
604
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1389 pages = {85--88}, |
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1390 publisher = {University of Victoria}, |
498
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1391 SOURCE = {OwnPublication}, |
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1392 PDF = {papers/2006_ismir_freedb.pdf}, |
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1393 } |
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1394 |
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1395 @INPROCEEDINGS{bergstra+mandel+eck:2010, |
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1396 author = {Bergstra, James and Mandel, Michael and Eck, Douglas}, |
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1397 title = {Scalable Genre and Tag Prediction with Spectral Covariance}, |
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1398 booktitle = {{ISMIR}}, |
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1399 year = {2010}, |
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1400 pages = {507--512}, |
498
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1401 } |
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1402 |
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1403 @MASTERSTHESIS{Bergstra-Msc-2006, |
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1404 author = {Bergstra, James}, |
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1405 keywords = {apprentissage statistique, classification de musique par genre, extraction de caract{\'{e}}ristiques sonores, recherche d'information musicale}, |
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1406 title = {Algorithms for Classifying Recorded Music by Genre}, |
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1407 year = {2006}, |
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1408 school = {Universit{\'{e}} de Montreal}, |
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1409 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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1410 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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1411 } |
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1412 |
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1413 @INPROCEEDINGS{bergstra:2010cosyne, |
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1414 author = {Bergstra, James and Bengio, Yoshua and Lamblin, Pascal and Desjardins, Guillaume and Louradour, Jerome}, |
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1415 title = {Image classification with complex cell neural networks}, |
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1416 booktitle = {Computational and systems neuroscience (COSYNE)}, |
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1417 year = {2010}, |
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1418 note = {Poster}, |
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1419 url = {http://www.frontiersin.org/conferences/individual_abstract_listing.php?conferid=770&pap=3626&ind_abs=1&pg=335}, |
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1420 doi = {10.3389/conf.fnins.2010.03.00334} |
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1421 } |
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1422 |
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1423 @INPROCEEDINGS{biaslearn:2000:ijcnn, |
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1424 author = {Ghosn, Joumana and Bengio, Yoshua}, |
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1425 title = {Bias Learning, Knowledge Sharing}, |
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1426 booktitle = {International Joint Conference on Neural Networks 2000}, |
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1427 volume = {I}, |
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1428 year = {2000}, |
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1429 pages = {9--14}, |
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1430 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/ijcnn_manifold.pdf}, |
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1431 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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1432 topics={MultiTask},cat={C}, |
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1433 } |
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1434 |
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1435 @ARTICLE{biaslearn:2003:tnn, |
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1436 author = {Ghosn, Joumana and Bengio, Yoshua}, |
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1437 title = {Bias Learning, Knowledge Sharing}, |
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1438 journal = {IEEE Transaction on Neural Networks}, |
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1439 volume = {14}, |
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1440 number = {4}, |
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1441 year = {2003}, |
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1442 pages = {748--765}, |
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1443 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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1444 topics={MultiTask},cat={J}, |
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1445 } |
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1446 |
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1447 @MASTERSTHESIS{Boisvert-Mcs-2005, |
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1448 author = {Boisvert, Maryse}, |
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1449 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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1450 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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1451 year = {2005}, |
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1452 school = {Universit{\'{e}} de Montr{\'{e}}al} |
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1453 } |
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1454 |
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1455 @INPROCEEDINGS{bonneville98, |
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1456 author = {Bonneville, Martin and Meunier, Jean and Bengio, Yoshua and Soucy, Jean-Paul}, |
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1457 title = {Support Vector Machines for Improving the classification of Brain Pet Images}, |
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1458 booktitle = {SPIE Medical Imaging}, |
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1459 year = {1998}, |
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1460 topics={Kernel},cat={C}, |
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1461 } |
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1462 |
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1463 @INPROCEEDINGS{Bottou+Bengio95, |
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1464 author = {Bottou, {L{\'{e}}on} and Bengio, Yoshua}, |
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1465 title = {Convergence Properties of the {K}-Means Algorithm}, |
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1466 year = {1995}, |
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1467 pages = {585--592}, |
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1468 crossref = {NIPS7-shorter}, |
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1469 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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1470 topics={Unsupervised},cat={C}, |
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1471 } |
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1472 |
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1473 @ARTICLE{bottou-98, |
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1474 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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1475 title = {High Quality Document Image Compression with {DjVu}}, |
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1476 journal = {Journal of Electronic Imaging}, |
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1477 volume = {7}, |
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1478 number = {3}, |
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1479 year = {1998}, |
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1480 pages = {410--425}, |
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1481 topics={Compression},cat={J}, |
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1482 } |
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1483 |
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1484 @INPROCEEDINGS{Bottou-dcc98, |
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1485 author = {Bottou, {L{\'{e}}on} and G. Howard, Paul and Bengio, Yoshua}, |
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1486 editor = {Society, {IEEE} Computer}, |
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1487 title = {The Z-Coder Adaptive Binary Coder}, |
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1488 booktitle = {Data Compression Conference}, |
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1489 year = {1998}, |
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1490 url = {http://leon.bottou.org/papers/bottou-howard-bengio-98}, |
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1491 topics={Compression},cat={C}, |
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1492 } |
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1493 |
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1494 @INPROCEEDINGS{bottou-lecun-bengio-97, |
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1495 author = {Bottou, {L{\'{e}}on} and {LeCun}, Yann and Bengio, Yoshua}, |
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1496 title = {Global Training of Document Processing Systems using Graph Transformer Networks}, |
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1497 booktitle = {Proc. of Computer Vision and Pattern Recognition}, |
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1498 year = {1997}, |
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1499 pages = {490--494}, |
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1500 publisher = {IEEE}, |
604
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1501 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/bottou-lecun-bengio-97.pdf}, |
498
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1502 topics={PriorKnowledge,Speech},cat={C}, |
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1503 } |
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1504 |
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1505 @TECHREPORT{bottou96TR, |
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1506 author = {Bottou, {L{\'{e}}on} and Bengio, Yoshua and {LeCun}, Yann}, |
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1507 title = {Document analysis with transducers}, |
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1508 number = {Technical Memorandum HA615600-960701-01TM}, |
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1509 year = {1996}, |
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1510 institution = {AT\&T Labs}, |
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1511 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/transducer-tm.ps.gz}, |
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1512 topics={HighDimensional},cat={T}, |
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1513 } |
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1514 |
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1515 @TECHREPORT{bottou97TR, |
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1516 author = {Bottou, {L{\'{e}}on} and Bengio, Yoshua and G. Howard, Paul}, |
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1517 title = {Z-Coder: A Fast Adaptive Binary Arithmetic Coder}, |
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1518 number = {Technical Memorandum HA615600-970721-02TM}, |
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1519 year = {1997}, |
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1520 institution = {AT\&T Labs}, |
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1521 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/zcoder-tm.ps.gz}, |
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1522 topics={Compression},cat={T}, |
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1523 } |
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1524 |
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1525 @MASTERSTHESIS{Bouchard-Msc-2007, |
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1526 author = {Bouchard, Lysiane}, |
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1527 keywords = {auditory cortex, fMRI, linear classifier, logistic regression, na{\"{\i}}ve bayesian gaussian model, neuroimaging, spectro-temporal modulation, support vectors machine}, |
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1528 title = {Analyse par apprentissage automatique des r{\'{e}}ponses fMRI du cortex auditif {\`{a}} des modulations spectro-temporelles.}, |
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1529 year = {2009}, |
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1530 school = {Universit{\'{e}} de Montr{\'{e}}al}, |
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1531 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).} |
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1532 } |
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1533 |
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1534 @PHDTHESIS{Boufaden-Phd-2005, |
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1535 author = {Boufaden, Narj{\`{e}}s}, |
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1536 title = {Extraction d’information {\`{a}} partir de transcriptions de conversations t{\'{e}}l{\'{e}}phoniques sp{\'{e}}cialis{\'{e}}es}, |
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1537 year = {2005}, |
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1538 school = {Universit{\'{e}} de Montr{\'{e}}al, D{\'{e}}partement d'Informatique et de Recherche Op{\'{e}}rationnel} |
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1539 } |
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1540 |
604
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1541 @TECHREPORT{Breuleux+al-TR-2010, |
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1542 author = {Breuleux, Olivier and Bengio, Yoshua and Vincent, Pascal}, |
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1543 title = {Unlearning for Better Mixing}, |
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1544 number = {1349}, |
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1545 year = {2010}, |
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1546 institution = {Universit{\'{e}} de Montr{\'{e}}al/DIRO}, |
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1547 abstract = {Two learning algorithms were recently proposed – Herding and Fast Persistent Contrastive Divergence (FPCD) – which share the following interesting characteristic: they exploit changes in the model parameters while sampling in order to escape modes and mix better, during the sampling process that is part of the learning algorithm. We first justify such approaches as ways to escape modes while approximately keeping the same asymptotic distribution of the {Markov} chain. We then extend FPCD using an idea borrowed from Herding in order to obtain a pure sampling algorithm and show empirically that this FPCD-sampler yields substantially better samples than Gibbs sampling. Because these algorithms entangle the model and the sampling algorithm and we want to evaluate both (but particularly how well the sampling schemes mix), it is not always easy to evaluate them, so we propose a “black-box” approach based on how well and how quickly the samples generated by a model “cover” the test set examples. We empirically study these algorithms and variations with this perspective and these new evaluation tools in order to better understand their strengths and limitations.} |
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1548 } |
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1549 |
498
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1550 @INPROCEEDINGS{Carreau+Bengio-2007, |
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1551 author = {Carreau, Julie and Bengio, Yoshua}, |
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1552 title = {A Hybrid {Pareto} Model for Conditional Density Estimation of Asymmetric Fat-Tail Data}, |
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1553 booktitle = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS'07)}, |
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1554 year = {2007}, |
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1555 publisher = {Omnipress}, |
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1556 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 |
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1557 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.}, |
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1558 date={21-24} |
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1559 } |
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1560 |
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1561 @ARTICLE{Carreau+Bengio-2009, |
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1562 author = {Carreau, Julie and Bengio, Yoshua}, |
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1563 title = {A Hybrid {Pareto} Mixture for Conditional Asymmetric Fat-Tailed Distribution}, |
498
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1564 journal = {IEEE Transactions on Neural Networks}, |
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1565 volume = {20}, |
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1566 number = {7}, |
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1567 year = {2009}, |
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1568 pages = {1087--1101}, |
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1569 issn = {1045-9227}, |
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1570 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.} |
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1571 } |
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1572 |
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1573 @ARTICLE{Carreau+Bengio-extreme-2009, |
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1574 author = {Carreau, Julie and Bengio, Yoshua}, |
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1575 title = {A Hybrid {Pareto} Model for Asymmetric Fat-Tailed Data: the univariate case}, |
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1576 journal = {Extremes}, |
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1577 volume = {12}, |
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1578 number = {1}, |
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1579 year = {2009}, |
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1580 pages = {53--76}, |
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1581 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.} |
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1582 } |
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1583 |
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1584 @PHDTHESIS{Carreau-PhD-2007, |
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1585 author = {Carreau, Julie}, |
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1586 keywords = {density estimation, extreme values, generalized {Pareto} distribution, heavy-tailed distribution, mixture of distributions, neural networks}, |
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1587 title = {Mod{\`{e}}les {Pareto} hybrides pour distributions asym{\'{e}}triques et {\`{a}} queues lourdes}, |
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1588 year = {2007}, |
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1589 school = {UdeM}, |
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1590 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.} |
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1591 } |
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1592 |
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1593 @INPROCEEDINGS{casagrande+eck+kegl:icmc2005, |
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1594 author = {Casagrande, Norman and Eck, Douglas and K{\'{e}}gl, Bal{\'{a}}zs}, |
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1595 title = {Geometry in Sound: A Speech/Music Audio Classifier Inspired by an Image Classifier}, |
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1596 booktitle = {{Proceedings of the International Computer Music Conference (ICMC)}}, |
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1597 year = {2005}, |
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1598 pages = {207--210}, |
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1599 url = {http://www.iro.umontreal.ca/~eckdoug/papers/2005_icmc_casagrande.pdf}, |
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1600 source={OwnPublication}, |
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1601 sourcetype={Conference}, |
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1602 } |
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1603 |
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1604 @INPROCEEDINGS{casagrande+eck+kegl:ismir2005, |
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1605 author = {Casagrande, Norman and Eck, Douglas and K{\'{e}}gl, Bal{\'{a}}zs}, |
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1606 title = {Frame-Level Audio Feature Extraction using {A}da{B}oost}, |
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1607 booktitle = {{Proceedings of the 6th International Conference on Music Information Retrieval ({ISMIR} 2005)}}, |
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1608 year = {2005}, |
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1609 pages = {345--350}, |
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1610 url = {http://www.iro.umontreal.ca/~eckdoug/papers/2005_ismir_casagrande.pdf}, |
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1611 source={OwnPublication}, |
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1612 sourcetype={Conference}, |
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1613 } |
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1614 |
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1615 @PROCEEDINGS{ccai2006, |
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1616 editor = {Lamontagne, Luc and Marchand, Mario}, |
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1617 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}, |
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1618 booktitle = {Canadian Conference on AI}, |
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1619 series = {Lecture Notes in Computer Science}, |
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1620 volume = {4013}, |
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1621 year = {2006}, |
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1622 publisher = {Springer} |
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1623 } |
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1624 |
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1625 @INPROCEEDINGS{Chapados+Bengio-2006, |
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1626 author = {Chapados, Nicolas and Bengio, Yoshua}, |
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1627 title = {The K Best-Paths Approach to Approximate Dynamic Programming with Application to Portfolio Optimization}, |
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1628 booktitle = {AI06}, |
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1629 year = {2006}, |
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1630 pages = {491-502} |
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1631 } |
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1632 |
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1633 @INPROCEEDINGS{Chapados+Bengio-2007, |
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1634 author = {Chapados, Nicolas and Bengio, Yoshua}, |
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1635 title = {Forecasting Commodity Contract Spreads with Gaussian Process}, |
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1636 booktitle = {13th Intarnational Conference on Computing in Economics and Finance}, |
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1637 year = {2007}, |
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1638 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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1639 } |
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1640 |
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1641 @ARTICLE{Chapados+Bengio-2008-JOC, |
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1642 author = {Chapados, Nicolas and Bengio, Yoshua}, |
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1643 title = {Noisy K Best-Paths for Approximate Dynamic Programming with Application to Portfolio Optimization}, |
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1644 journal = {Journal of Computers}, |
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1645 volume = {2}, |
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1646 number = {1}, |
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1647 year = {2007}, |
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1648 pages = {12--19}, |
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1649 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 |
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1650 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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1651 } |
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1652 |
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1653 @MASTERSTHESIS{Chapados-Msc-2000, |
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1654 author = {Chapados, Nicolas}, |
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1655 title = {Crit{\`{e}}res d'optimisation d'algorithmes d'apprentissage en gestion de portefeuille}, |
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1656 year = {2000}, |
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1657 school = {Universit{\'{e}} de Montr{\'{e}}al} |
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1658 } |
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1659 |
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1660 @INPROCEEDINGS{chapados2000, |
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1661 author = {Chapados, Nicolas and Bengio, Yoshua}, |
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1662 title = {Cost Functions and Model Combination for {VaR}-Based Asset Allocation Using Neural Networks}, |
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1663 booktitle = {Computational Finance 2000}, |
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1664 year = {2000}, |
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1665 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/compfin2000_final.pdf}, |
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1666 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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1667 topics={Finance},cat={C}, |
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1668 } |
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1669 |
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1670 @ARTICLE{chapados:2001, |
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1671 author = {Chapados, Nicolas and Bengio, Yoshua}, |
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1672 title = {Cost Functions and Model Combination for VaR--based Asset Allocation using Neural Networks}, |
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1673 journal = {IEEE Transactions on Neural Networks}, |
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1674 volume = {12}, |
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1675 number = {4}, |
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1676 year = {2001}, |
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1677 pages = {890--906}, |
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1678 abstract = {We introduce an asset-allocation framework based on the active control of the value-at-risk of the portfolio. Within this framework, we |
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1679 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 |
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1680 using both paradigms are significantly outperforming the benchmark market performance.}, |
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1681 topics={Finance},cat={J}, |
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1682 } |
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1683 |
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1684 @ARTICLE{chapados:2003, |
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1685 author = {Bengio, Yoshua and Chapados, Nicolas}, |
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1686 title = {Extensions to Metric-Based Model Selection}, |
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1687 year = {2003}, |
604
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1688 crossref = {JMLR-shorter}, |
498
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1689 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.}, |
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1690 topics={ModelSelection,Finance},cat={J}, |
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1691 } |
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1692 |
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1693 @ARTICLE{chapelle:2001, |
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1694 author = {Chapelle, Olivier and Vapnik, Vladimir and Bengio, Yoshua}, |
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1695 title = {Model Selection for Small Sample Regression}, |
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1696 journal = {Machine Learning}, |
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1697 year = {2001}, |
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1698 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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1699 topics={ModelSelection},cat={J}, |
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1700 } |
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1701 |
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1702 @INCOLLECTION{chapter-eval-longterm-2001, |
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1703 author = {Schmidhuber, Juergen and Hochreiter, Sepp and Bengio, Yoshua}, |
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1704 editor = {Kolen, J. and Kremer, S.}, |
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1705 title = {Evaluating Benchmark Problems by Random Guessing}, |
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1706 booktitle = {Field Guide to Dynamical Recurrent Networks}, |
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1707 year = {2001}, |
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1708 publisher = {IEEE Press}, |
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1709 topics={LongTerm},cat={B}, |
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1710 } |
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1711 |
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1712 @INCOLLECTION{chapter-gradient-document-2001, |
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1713 author = {{LeCun}, Yann and Bottou, {L{\'{e}}on} and Bengio, Yoshua and Haffner, Patrick}, |
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1714 editor = {Haykin, S. and Kosko, B.}, |
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1715 title = {Gradient-Based Learning Applied to Document Recognition}, |
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1716 booktitle = {Intelligent Signal Processing}, |
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1717 year = {2001}, |
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1718 pages = {306--351}, |
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1719 publisher = {IEEE Press}, |
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1720 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/lecun-01a.pdf}, |
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1721 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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1722 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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1723 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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1724 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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1725 topics={PriorKnowledge,Speech},cat={B}, |
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1726 } |
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1727 |
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1728 @INCOLLECTION{chapter-gradient-flow-2001, |
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1729 author = {Hochreiter, Sepp and Bengio, Yoshua and Frasconi, Paolo}, |
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1730 editor = {Kolen, J. and Kremer, S.}, |
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1731 title = {Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies}, |
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1732 booktitle = {Field Guide to Dynamical Recurrent Networks}, |
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1733 year = {2001}, |
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1734 publisher = {IEEE Press}, |
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1735 topics={LongTerm},cat={B}, |
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1736 } |
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1737 |
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1738 @INPROCEEDINGS{chemero+eck:1999, |
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1739 author = {Chemero, T. and Eck, Douglas}, |
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1740 title = {An Exploration of Representational Complexity via Coupled Oscillators}, |
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1741 booktitle = {{Proceedings of the Tenth Midwest Artificial Intelligence and Cognitive Science Society}}, |
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1742 year = {1999}, |
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1743 publisher = {MIT Press}, |
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1744 url = {http://www.iro.umontreal.ca/~eckdoug/papers/1999_chemero.pdf}, |
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1745 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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1746 source={OwnPublication}, |
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1747 sourcetype={Conference}, |
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1748 } |
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1749 |
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1750 @ARTICLE{ChemInfModel2006, |
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1751 author = {Erhan, Dumitru and {L'Heureux}, Pierre-Jean and Yue, Shi Yi and Bengio, Yoshua}, |
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1752 title = {Collaborative Filtering on a Family of Biological Targets}, |
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1753 journal = {J. Chem. Inf. Model.}, |
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1754 volume = {46}, |
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1755 number = {2}, |
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1756 year = {2006}, |
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1757 pages = {626--635}, |
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1758 abstract = {Building a QSAR model of a new biological target for which few screening data are available is a statistical |
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1759 challenge. However, the new target may be part of a bigger family, for which we have more screening data. |
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1760 Collaborative filtering or, more generally, multi-task learning, is a machine learning approach that improves |
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1761 the generalization performance of an algorithm by using information from related tasks as an inductive |
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1762 bias. We use collaborative filtering techniques for building predictive models that link multiple targets to |
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1763 multiple examples. The more commonalities between the targets, the better the multi-target model that can |
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1764 be built. We show an example of a multi-target neural network that can use family information to produce |
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1765 a predictive model of an undersampled target. We evaluate JRank, a kernel-based method designed for |
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1766 collaborative filtering. We show their performance on compound prioritization for an HTS campaign and |
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1767 the underlying shared representation between targets. JRank outperformed the neural network both in the |
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1768 single- and multi-target models.}, |
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1769 topics={Bioinformatic,MultiTask},cat={J}, |
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1770 } |
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1771 |
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1772 @TECHREPORT{collobert:2001:rr01-12, |
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1773 author = {Collobert, Ronan and Bengio, Samy and Bengio, Yoshua}, |
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1774 title = {A Parallel Mixture of {SVM}s for Very Large Scale Problems}, |
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1775 number = {12}, |
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1776 year = {2001}, |
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1777 institution = {IDIAP}, |
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1778 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/IDIAP-RR-01-12.ps}, |
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1779 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.}, |
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1780 topics={Kernel},cat={T}, |
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1781 } |
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1782 |
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1783 @ARTICLE{collobert:2002, |
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1784 author = {Collobert, Ronan and Bengio, Samy and Bengio, Yoshua}, |
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1785 title = {Parallel Mixture of {SVM}s for Very Large Scale Problem}, |
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1786 journal = {Neural Computation}, |
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1787 year = {2002}, |
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1788 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.}, |
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1789 topics={HighDimensional,Kernel},cat={J}, |
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1790 } |
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1791 |
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1792 @BOOK{collobert:2002:book, |
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1793 author = {Collobert, Ronan and Bengio, Yoshua and Bengio, Samy}, |
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1794 editor = {Lee, S. W. and Verri, A.}, |
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1795 title = {Scaling Large Learning Problems with Hard Parallel Mixtures}, |
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1796 booktitle = {Pattern Recognition with Support Vector Machines}, |
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1797 series = {Lecture Notes in Computer Science}, |
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1798 volume = {2388}, |
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1799 year = {2002}, |
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1800 publisher = {Springer-Verlag}, |
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1801 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/2002_mixtures_svm.pdf}, |
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1802 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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1803 topics={Kernel},cat={B}, |
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1804 } |
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1805 |
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1806 @MISC{copyright-CTAI, |
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1807 author = {Bengio, Yoshua and Ducharme, R{\'{e}}jean and Dorion, Christian}, |
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1808 title = {Commodity Trading Advisor Index}, |
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1809 year = {2004-2009}, |
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1810 howpublished = {copyright, and commercialized software license.} |
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1811 } |
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1812 |
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1813 @MISC{copyright-PLearn, |
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1814 author = {Vincent, Pascal and Bengio, Yoshua}, |
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1815 title = {{PLearn}, a {C++} Machine Learning Library}, |
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1816 year = {1998-2009}, |
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1817 howpublished = {copyright, public domain license.}, |
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1818 url = {www.plearn.org} |
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1819 } |
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1820 |
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1821 @ARTICLE{Cosi90, |
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1822 author = {Cosi, Piero and Bengio, Yoshua and De Mori, Renato}, |
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1823 title = {Phonetically-based multi-layered networks for acoustic property extraction and automatic speech recognition}, |
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1824 journal = {Speech Communication}, |
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1825 volume = {9}, |
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1826 number = {1}, |
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1827 year = {1990}, |
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1828 pages = {15--30}, |
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1829 topics={PriorKnowledge,Speech},cat={J}, |
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1830 } |
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1831 |
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1832 @INCOLLECTION{courville+eck+bengio:nips2009, |
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1833 author = {Courville, Aaron and Eck, Douglas and Bengio, Yoshua}, |
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1834 editor = {}, |
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1835 title = {An Infinite Factor Model Hierarchy Via a Noisy-Or Mechanism}, |
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1836 booktitle = {Neural Information Processing Systems Conference (NIPS) 22}, |
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1837 year = {2009}, |
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1838 pages = {405--413}, |
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1839 publisher = {}, |
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1840 url = {http://books.nips.cc/papers/files/nips22/NIPS2009_1100.pdf}, |
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1841 source={OwnPublication}, |
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1842 sourcetype={Conference}, |
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1843 pdf={""}, |
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1844 } |
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1845 |
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1846 @INPROCEEDINGS{davies+plumbley+eck:waspaa2009, |
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1847 author = {Davies, M. and Plumbley, M. and Eck, Douglas}, |
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1848 title = {Towards a musical beat emphasis function}, |
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1849 booktitle = {Proceedings of IEEE WASPAA}, |
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1850 year = {2009}, |
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1851 organization = {IEEE Workshop on Applications of Signal Processing to Audio and Acoustics}, |
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1852 source={OwnPublication}, |
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1853 sourcetype={Conference}, |
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1854 } |
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1855 |
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1856 @INPROCEEDINGS{Delalleau+al-2005, |
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1857 author = {Delalleau, Olivier and Bengio, Yoshua and Le Roux, Nicolas}, |
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1858 editor = {Cowell, Robert G. and Ghahramani, Zoubin}, |
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1859 title = {Efficient Non-Parametric Function Induction in Semi-Supervised Learning}, |
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1860 booktitle = {Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics (AISTATS'05)}, |
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1861 year = {2005}, |
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1862 pages = {96--103}, |
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1863 publisher = {Society for Artificial Intelligence and Statistics}, |
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1864 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/semisup_aistats2005.pdf}, |
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1865 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.}, |
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1866 topics={Unsupervised},cat={C}, |
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1867 } |
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1868 |
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1869 @INCOLLECTION{Delalleau+al-ssl-2006, |
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1870 author = {Delalleau, Olivier and Bengio, Yoshua and Le Roux, Nicolas}, |
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1871 editor = {Chapelle, Olivier and {Sch{\"{o}}lkopf}, Bernhard and Zien, Alexander}, |
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1872 title = {Large-Scale Algorithms}, |
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1873 booktitle = {Semi-Supervised Learning}, |
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1874 year = {2006}, |
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1875 pages = {333--341}, |
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1876 publisher = {{MIT} Press}, |
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1877 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/delalleau_ssl.pdf}, |
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1878 abstract = {In Chapter 11, it is shown how a number of graph-based semi-supervised learning |
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1879 algorithms can be seen as the minimization of a specific cost function, leading to a |
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1880 linear system with n equations and unknowns (with n the total number of labeled |
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1881 and unlabeled examples). Solving such a linear system will in general require on the |
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1882 order of O(kn2) time and O(kn) memory (for a sparse graph where each data point |
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1883 has k neighbors), which can be prohibitive on large datasets (especially if k = n, |
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1884 i.e. the graph is dense). We present in this chapter a subset selection method that |
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1885 can be used to reduce the original system to one of size m << n. The idea is to solve |
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1886 for the labels of a subset S of X of only m points, while still retaining information |
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1887 from the rest of the data by approximating their label with a linear combination of |
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1888 the labels in S (using the induction formula presented in Chapter 11). This leads |
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1889 to an algorithm whose computational requirements scale as O(m2n) and memory |
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1890 requirements as O(m2), thus allowing one to take advantage of significantly bigger |
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1891 unlabeled datasets than with the original algorithms.}, |
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1892 cat={B},topics={Unsupervised}, |
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1893 } |
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1894 |
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1895 @INCOLLECTION{DeMori90a, |
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1896 author = {De Mori, Renato and Bengio, Yoshua and Cosi, Piero}, |
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1897 editor = {Mohr, R. and Pavlidis, T. and Sanfelin, A.}, |
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1898 title = {On the use of an ear model and multi-layer networks for automatic speech recognition}, |
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1899 booktitle = {Structural Pattern Analysis}, |
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1900 year = {1990}, |
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1901 publisher = {World Scientific}, |
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1902 topics={PriorKnowledge,Speech},cat={B}, |
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1903 } |
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1904 |
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1905 @INPROCEEDINGS{Desjardins+al-2010, |
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1906 author = {Desjardins, Guillaume and Courville, Aaron and Bengio, Yoshua}, |
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1907 title = {Tempered {Markov} Chain Monte Carlo for training of Restricted {Boltzmann} Machine}, |
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1908 booktitle = {JMLR W\&CP: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS 2010)}, |
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1909 volume = {9}, |
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1910 year = {2010}, |
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1911 pages = {145--152}, |
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1912 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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1913 } |
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1914 |
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1915 @TECHREPORT{Desjardins-2008, |
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1916 author = {Desjardins, Guillaume and Bengio, Yoshua}, |
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1917 keywords = {Convolutional Architectures, Deep Networks, RBM, Vision}, |
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1918 title = {Empirical Evaluation of Convolutional RBMs for Vision}, |
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1919 number = {1327}, |
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1920 year = {2008}, |
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1921 institution = {D{\'{e}}partement d'Informatique et de Recherche Op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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1922 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 |
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1923 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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1924 } |
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1925 |
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1926 @TECHREPORT{Desjardins-tech-2009, |
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1927 author = {Desjardins, Guillaume and Courville, Aaron and Bengio, Yoshua and Vincent, Pascal and Delalleau, Olivier}, |
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1928 keywords = {CD, PCD, RBM, simulated tempering, tempered MCMC, unsupervised learning}, |
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1929 title = {Tempered {Markov} Chain Monte Carlo for training of Restricted {Boltzmann} Machines}, |
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1930 number = {1345}, |
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1931 year = {2009}, |
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1932 institution = {D{\'{e}}partement d'Informatique et de Recherche Op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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1933 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 |
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1934 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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1935 } |
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1936 |
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1937 @ARTICLE{Dugas+Bengio-2009, |
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1938 author = {Dugas, Charles and Bengio, Yoshua and Belisle, Francois and Nadeau, Claude and Garcia, Rene}, |
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1939 title = {Incorporating Functional Knowledge in Neural Networks}, |
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1940 journal = {The Journal of Machine Learning Research}, |
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1941 volume = {10}, |
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1942 year = {2009}, |
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1943 pages = {1239--1262}, |
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1944 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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1945 } |
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1946 |
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1947 @PHDTHESIS{Dugas-Phd-2003, |
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1948 author = {Dugas, Charles}, |
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1949 title = {Les algorithmes d'apprentissage appliqu{\'{e}}s aux risques financiers}, |
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1950 year = {2003}, |
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1951 school = {Universit{\'{e}} de Montr{\'{e}}al} |
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1952 } |
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1953 |
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1954 @ARTICLE{dugas:2003, |
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1955 author = {Dugas, Charles and Bengio, Yoshua and Chapados, Nicolas and Vincent, Pascal and Denoncourt, Germain and Fournier, Christian}, |
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1956 title = {Statistical Learning Algorithms Applied to Automobile Insurance Ratemaking}, |
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1957 journal = {CAS Forum}, |
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1958 volume = {1}, |
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1959 number = {1}, |
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1960 year = {2003}, |
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1961 pages = {179--214}, |
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1962 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.}, |
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1963 topics={Finance,Mining},cat={J}, |
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1964 } |
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1965 |
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1966 @INPROCEEDINGS{eck+bertinmahieux+lamere+green:nips2007, |
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1967 author = {Eck, Douglas and Lamere, Paul and Bertin-Mahieux, Thierry and Green, Stephen}, |
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1968 editor = {Platt, John and Kolen, J. and Singer, Yoram and Roweis, S.}, |
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1969 title = {Automatic Generation of Social Tags for Music Recommendation}, |
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1970 year = {2008}, |
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1971 crossref = {NIPS20-shorter}, |
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1972 source = "OwnPublication" |
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1973 } |
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1974 |
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1975 @INPROCEEDINGS{eck+bertinmahieux+lamere:ismir2007, |
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1976 author = {Eck, Douglas and Bertin-Mahieux, Thierry and Lamere, Paul}, |
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1977 title = {Autotagging music using supervised machine learning}, |
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1978 booktitle = {{Proceedings of the 8th International Conference on Music Information Retrieval ({ISMIR} 2007)}}, |
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1979 year = {2007}, |
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1980 source={OwnPublication}, |
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1981 } |
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1982 |
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1983 @INPROCEEDINGS{eck+casagrande:ismir2005, |
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1984 author = {Eck, Douglas and Casagrande, Norman}, |
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1985 title = {Finding Meter in Music Using an Autocorrelation Phase Matrix and Shannon Entropy}, |
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1986 booktitle = {{Proceedings of the 6th International Conference on Music Information Retrieval ({ISMIR} 2005)}}, |
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1987 year = {2005}, |
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1988 pages = {504--509}, |
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1989 url = {http://www.iro.umontreal.ca/~eckdoug/papers/2005_ismir.pdf}, |
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1990 source={OwnPublication}, |
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1991 sourcetype={Conference}, |
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1992 } |
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1993 |
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1994 @INCOLLECTION{eck+gasser+port:2000, |
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1995 author = {Eck, Douglas and Gasser, M. and Port, Robert}, |
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1996 editor = {Desain, P. and Windsor, L.}, |
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1997 title = {Dynamics and Embodiment in Beat Induction}, |
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1998 booktitle = {{Rhythm Perception and Production}}, |
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1999 year = {2000}, |
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2000 pages = {157--170}, |
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2001 publisher = {Swets and Zeitlinger}, |
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2002 url = {http://www.iro.umontreal.ca/~eckdoug/papers/2000_rppw.pdf}, |
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2003 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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2004 source={OwnPublication}, |
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2005 sourcetype={Chapter}, |
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2006 } |
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2007 |
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2008 @INPROCEEDINGS{eck+gasser:1996, |
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2009 author = {Eck, Douglas and Gasser, M.}, |
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2010 editor = {}, |
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2011 title = {Perception of Simple Rhythmic Patterns in a Network of Oscillators}, |
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2012 booktitle = {{The Proceedings of the Eighteenth Annual Conference of the Cognitive Science Society}}, |
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2013 year = {1996}, |
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2014 publisher = {Lawrence Erlbaum Associates}, |
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2015 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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2016 source={OwnPublication}, |
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2017 sourcetype={Conference}, |
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2018 } |
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2019 |
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2020 @TECHREPORT{eck+graves+schmidhuber:tr-speech2003, |
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2021 author = {Eck, Douglas and Graves, A. and Schmidhuber, Juergen}, |
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2022 title = {A New Approach to Continuous Speech Recognition Using {LSTM} Recurrent Neural Networks}, |
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2023 number = {IDSIA-14-03}, |
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2024 year = {2003}, |
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2025 institution = {IDSIA}, |
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2026 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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2027 source={OwnPublication}, |
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2028 sourcetype={TechReport}, |
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2029 } |
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2030 |
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2031 @TECHREPORT{eck+lapalme:2008, |
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2032 author = {Eck, Douglas and Lapalme, J.}, |
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2033 title = {Learning Musical Structure Directly from Sequences of Music}, |
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2034 number = {1300}, |
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2035 year = {2008}, |
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2036 institution = {Universit{\'{e}} de Montr{\'{e}}al DIRO}, |
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2037 url = {http://www.iro.umontreal.ca/~eckdoug/papers/tr1300.pdf}, |
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2038 source={OwnPublication}, |
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2039 sourcetype={TechReport}, |
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2040 } |
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2041 |
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2042 @INPROCEEDINGS{eck+schmidhuber:icann2002, |
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2043 author = {Eck, Douglas and Schmidhuber, Juergen}, |
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2044 editor = {Dorronsoro, J.}, |
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2045 title = {Learning The Long-Term Structure of the Blues}, |
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2046 booktitle = {{Artificial Neural Networks -- ICANN 2002 (Proceedings)}}, |
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2047 volume = {}, |
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2048 year = {2002}, |
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2049 pages = {284--289}, |
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2050 publisher = {Springer}, |
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2051 url = {http://www.iro.umontreal.ca/~eckdoug/papers/2002_icannMusic.pdf}, |
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2052 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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2053 source={OwnPublication}, |
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2054 sourcetype={Conference}, |
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2055 } |
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2056 |
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2057 @INPROCEEDINGS{eck+schmidhuber:ieee2002, |
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2058 author = {Eck, Douglas and Schmidhuber, Juergen}, |
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2059 editor = {Bourlard, H.}, |
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2060 title = {Finding Temporal Structure in Music: Blues Improvisation with {LSTM} Recurrent Networks}, |
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2061 booktitle = {Neural Networks for Signal Processing XII, Proceedings of the 2002 IEEE Workshop}, |
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2062 year = {2002}, |
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2063 pages = {747--756}, |
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2064 publisher = {IEEE}, |
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2065 url = {http://www.iro.umontreal.ca/~eckdoug/papers/2002_ieee.pdf}, |
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2066 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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2067 source={OwnPublication}, |
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2068 sourcetype={Conference}, |
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2069 } |
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2070 |
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2071 @ARTICLE{eck+scott:2005, |
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2072 author = {Eck, Douglas and Scott, S. K.}, |
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2073 title = {Editorial: New Research in Rhythm Perception and Production}, |
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2074 journal = {Music Perception}, |
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2075 volume = {22}, |
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2076 number = {3}, |
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2077 year = {2005}, |
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2078 pages = {371-388}, |
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2079 source={OwnPublication}, |
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2080 sourcetype={Other}, |
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2081 } |
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2082 |
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2083 @MISC{eck+scott:editor2005, |
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2084 author = {Eck, Douglas and Scott, S. K.}, |
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2085 title = {Music Perception}, |
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2086 year = {2005}, |
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2087 note = {Guest Editor, Special Issue on Rhythm Perception and Production, 22(3)}, |
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2088 source={OwnPublication}, |
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2089 sourcetype={Other}, |
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2090 } |
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2091 |
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2092 @INPROCEEDINGS{eck:1999, |
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2093 author = {Eck, Douglas}, |
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2094 editor = {}, |
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2095 title = {Learning Simple Metrical Preferences in a Network of {F}itzhugh-{N}agumo Oscillators}, |
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2096 booktitle = {{The Proceedings of the Twenty-First Annual Conference of the Cognitive Science Society}}, |
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2097 year = {1999}, |
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2098 publisher = {Lawrence Erlbaum Associates}, |
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2099 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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2100 source={OwnPublication}, |
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2101 sourcetype={Conference}, |
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2102 } |
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2103 |
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2104 @UNPUBLISHED{eck:bramsworkshop2004, |
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2105 author = {Eck, Douglas}, |
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2106 title = {Challenges for Machine Learning in the Domain of Music}, |
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2107 year = {2004}, |
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2108 note = {BRAMS Workshop on Brain and Music, Montreal Neurological Institute}, |
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2109 abstract = {Slides and musical examples available on request.}, |
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2110 source={OwnPublication}, |
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2111 sourcetype={Workshop}, |
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2112 optkey={""}, |
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2113 optmonth={""}, |
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2114 optannote={""}, |
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2115 } |
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2116 |
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2117 @PHDTHESIS{eck:diss, |
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2118 author = {Eck, Douglas}, |
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2119 title = {{Meter Through Synchrony: Processing Rhythmical Patterns with Relaxation Oscillators}}, |
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2120 year = {2000}, |
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2121 school = {Indiana University, Bloomington, IN, www.idsia.ch/\-\~{}doug/\-publications.html}, |
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2122 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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2123 source={OwnPublication}, |
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2124 sourcetype={Thesis}, |
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2125 } |
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2126 |
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2127 @INPROCEEDINGS{eck:icann2001, |
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2128 author = {Eck, Douglas}, |
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2129 editor = {Dorffner, Georg}, |
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2130 title = {A Network of Relaxation Oscillators that Finds Downbeats in Rhythms}, |
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2131 booktitle = {{Artificial Neural Networks -- ICANN 2001 (Proceedings)}}, |
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2132 volume = {}, |
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2133 year = {2001}, |
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2134 pages = {1239--1247}, |
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2135 publisher = {Springer}, |
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2136 url = {http://www.iro.umontreal.ca/~eckdoug/papers/2001_icann.pdf}, |
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2137 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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2138 source={OwnPublication}, |
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2139 sourcetype={Conference}, |
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2140 } |
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2141 |
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2142 @INPROCEEDINGS{eck:icassp2007, |
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2143 author = {Eck, Douglas}, |
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2144 editor = {}, |
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2145 title = {Beat Tracking Using an Autocorrelation Phase Matrix}, |
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2146 booktitle = {{Proceedings of the 2007 International Conference on Acoustics, Speech and Signal Processing (ICASSP)}}, |
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2147 year = {2007}, |
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2148 pages = {1313--1316}, |
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2149 publisher = {IEEE Signal Processing Society}, |
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2150 url = {http://www.iro.umontreal.ca/~eckdoug/papers/2007_icassp.pdf}, |
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2151 source={OwnPublication}, |
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2152 sourcetype={Conference}, |
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2153 } |
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2154 |
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2155 @INPROCEEDINGS{eck:icmpc2004, |
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2156 author = {Eck, Douglas}, |
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2157 editor = {Lipscomb, S. D. and Ashley, R. and Gjerdingen, R. O. and Webster, P.}, |
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2158 title = {A Machine-Learning Approach to Musical Sequence Induction That Uses Autocorrelation to Bridge Long Timelags}, |
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2159 booktitle = {{The Proceedings of the Eighth International Conference on Music Perception and Cognition ({ICMPC}8)}}, |
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2160 year = {2004}, |
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2161 pages = {542-543}, |
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2162 publisher = {Causal Productions}, |
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2163 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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2164 source={OwnPublication}, |
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2165 sourcetype={Conference}, |
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2166 } |
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2167 |
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2168 @INPROCEEDINGS{eck:icmpc2006, |
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2169 author = {Eck, Douglas}, |
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2170 editor = {Baroni, M. and Addessi, A. R. and Caterina, R. and Costa, M.}, |
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2171 title = {Beat Induction Using an Autocorrelation Phase Matrix}, |
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2172 booktitle = {The Proceedings of the 9th International Conference on Music Perception and Cognition ({ICMPC9})}, |
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2173 year = {2006}, |
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2174 pages = {931-932}, |
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2175 publisher = {Causal Productions}, |
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2176 source={OwnPublication}, |
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2177 sourcetype={Conference}, |
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2178 } |
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2179 |
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2180 @UNPUBLISHED{eck:irisworkshop2004, |
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2181 author = {Eck, Douglas}, |
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2182 title = {Using Autocorrelation to Bridge Long Timelags when Learning Sequences of Music}, |
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2183 year = {2004}, |
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2184 note = {IRIS 2004 Machine Learning Workshop, Ottawa, Canada}, |
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2185 abstract = {Slides and musical examples available on request.}, |
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2186 source={OwnPublication}, |
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2187 sourcetype={Workshop}, |
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2188 optkey={""}, |
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2189 optmonth={""}, |
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2190 optannote={""}, |
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2191 } |
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2192 |
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2193 @ARTICLE{eck:jnmr2001, |
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2194 author = {Eck, Douglas}, |
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2195 title = {A Positive-Evidence Model for Rhythmical Beat Induction}, |
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2196 journal = {Journal of New Music Research}, |
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2197 volume = {30}, |
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2198 number = {2}, |
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2199 year = {2001}, |
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2200 pages = {187--200}, |
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2201 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.}, |
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2202 source={OwnPublication}, |
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2203 sourcetype={Journal}, |
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2204 } |
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2205 |
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2206 @UNPUBLISHED{eck:mipsworkshop2004, |
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2207 author = {Eck, Douglas}, |
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2208 title = {Bridging Long Timelags in Music}, |
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2209 year = {2004}, |
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2210 note = {NIPS 2004 Workshop on Music and Machine Learning (MIPS), Whistler, British Columbia}, |
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2211 abstract = {Slides and musical examples available on request.}, |
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2212 source={OwnPublication}, |
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2213 sourcetype={Workshop}, |
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2214 optkey={""}, |
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2215 optmonth={""}, |
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2216 optannote={""}, |
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2217 } |
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2218 |
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2219 @ARTICLE{eck:mp2006, |
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2220 author = {Eck, Douglas}, |
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2221 title = {Finding Long-Timescale Musical Structure with an Autocorrelation Phase Matrix}, |
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2222 journal = {Music Perception}, |
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2223 volume = {24}, |
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2224 number = {2}, |
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2225 year = {2006}, |
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2226 pages = {167--176}, |
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2227 source={OwnPublication}, |
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2228 sourcetype={Journal}, |
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2229 } |
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2230 |
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2231 @UNPUBLISHED{eck:nipsworkshop2003, |
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2232 author = {Eck, Douglas}, |
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2233 title = {Time-warped hierarchical structure in music and speech: A sequence prediction challenge}, |
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2234 year = {2003}, |
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2235 note = {NIPS 2003 Workshop on Recurrent Neural Networks, Whistler, British Columbia}, |
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2236 abstract = {Slides and musical examples available on request.}, |
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2237 source={OwnPublication}, |
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2238 sourcetype={Workshop}, |
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2239 optkey={""}, |
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2240 optmonth={""}, |
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2241 optannote={""}, |
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2242 } |
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2243 |
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2244 @UNPUBLISHED{eck:nipsworkshop2006, |
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2245 author = {Eck, Douglas}, |
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2246 title = {Generating music sequences with an echo state network}, |
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2247 year = {2006}, |
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2248 note = {NIPS 2006 Workshop on Echo State Networks and Liquid State Machines}, |
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2249 abstract = {Slides and musical examples available on request.}, |
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2250 source={OwnPublication}, |
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2251 sourcetype={Workshop}, |
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2252 optkey={""}, |
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2253 optmonth={""}, |
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2254 optannote={""}, |
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2255 } |
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2256 |
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2257 @UNPUBLISHED{eck:nipsworkshop2007, |
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2258 author = {Eck, Douglas}, |
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2259 title = {Measuring and modeling musical expression}, |
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2260 year = {2007}, |
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2261 note = {NIPS 2007 Workshop on Music, Brain and Cognition}, |
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2262 source={OwnPublication}, |
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2263 sourcetype={Workshop}, |
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2264 optkey={""}, |
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2265 optmonth={""}, |
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2266 optannote={""}, |
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2267 } |
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2268 |
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2269 @ARTICLE{eck:psyres2002, |
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2270 author = {Eck, Douglas}, |
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2271 title = {Finding Downbeats with a Relaxation Oscillator}, |
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2272 journal = {Psychol. Research}, |
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2273 volume = {66}, |
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2274 number = {1}, |
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2275 year = {2002}, |
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2276 pages = {18--25}, |
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2277 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.}, |
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2278 source={OwnPublication}, |
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2279 sourcetype={Journal}, |
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2280 } |
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2281 |
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2282 @UNPUBLISHED{eck:rppw2005, |
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2283 author = {Eck, Douglas}, |
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2284 title = {Meter and Autocorrelation}, |
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2285 year = {2005}, |
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2286 note = {{10th Rhythm Perception and Production Workshop (RPPW), Alden Biesen, Belgium}}, |
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2287 source={OwnPublication}, |
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2288 sourcetype={Workshop}, |
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2289 } |
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2290 |
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2291 @TECHREPORT{eck:tr-music2002, |
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2292 author = {Eck, Douglas and Schmidhuber, Juergen}, |
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2293 title = {A First Look at Music Composition using {LSTM} Recurrent Neural Networks}, |
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2294 number = {IDSIA-07-02}, |
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2295 year = {2002}, |
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2296 institution = {IDSIA}, |
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2297 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.}}, |
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2298 source={OwnPublication}, |
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2299 sourcetype={TechReport}, |
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2300 } |
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2301 |
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2302 @TECHREPORT{eck:tr-npos2000, |
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2303 author = {Eck, Douglas}, |
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2304 title = {A Positive-Evidence Model for Classifying Rhythmical Patterns}, |
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2305 number = {IDSIA-09-00}, |
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2306 year = {2000}, |
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2307 institution = {IDSIA}, |
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2308 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.}}, |
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2309 ps={ftp://ftp.idsia.ch/pub/techrep/IDSIA-09-00.ps.gz}, |
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2310 source={OwnPublication}, |
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2311 sourcetype={TechReport}, |
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2312 } |
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2313 |
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2314 @TECHREPORT{eck:tr-oscnet2001, |
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2315 author = {Eck, Douglas}, |
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2316 title = {A Network of Relaxation Oscillators that Finds Downbeats in Rhythms}, |
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2317 number = {IDSIA-06-01}, |
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2318 year = {2001}, |
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2319 institution = {IDSIA}, |
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2320 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.}}, |
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2321 ps={ftp://ftp.idsia.ch/pub/techrep/IDSIA-06-01.ps.gz}, |
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2322 source={OwnPublication}, |
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2323 sourcetype={TechReport}, |
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2324 } |
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2325 |
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2326 @TECHREPORT{eck:tr-tracking2000, |
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2327 author = {Eck, Douglas}, |
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2328 title = {Tracking Rhythms with a Relaxation Oscillator}, |
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2329 number = {IDSIA-10-00}, |
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2330 year = {2000}, |
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2331 institution = {IDSIA}, |
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2332 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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2333 ps={ftp://ftp.idsia.ch/pub/techrep/IDSIA-10-00.ps.gz}, |
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2334 source={OwnPublication}, |
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2335 sourcetype={TechReport}, |
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2336 } |
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2337 |
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2338 @TECHREPORT{eck:tr-tracking2002, |
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2339 author = {Eck, Douglas}, |
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2340 title = {Real-Time Musical Beat Induction with Spiking Neural Networks}, |
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2341 number = {IDSIA-22-02}, |
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2342 year = {2002}, |
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2343 institution = {IDSIA}, |
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2344 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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2345 source={OwnPublication}, |
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2346 sourcetype={TechReport}, |
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2347 } |
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2348 |
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2349 @UNPUBLISHED{eck:verita2002, |
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2350 author = {Eck, Douglas}, |
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2351 title = {Real Time Beat Induction with Spiking Neurons}, |
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2352 year = {2002}, |
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2353 note = {{Music, Motor Control and the Mind: Symposium at Monte Verita, May}}, |
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2354 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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2355 source={OwnPublication}, |
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2356 sourcetype={Workshop}, |
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2357 } |
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2358 |
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2359 @INPROCEEDINGS{ElHihi+Bengio-nips8, |
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2360 author = {El Hihi, Salah and Bengio, Yoshua}, |
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2361 title = {Hierarchical Recurrent Neural Networks for Long-Term Dependencies}, |
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2362 year = {1996}, |
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2363 crossref = {NIPS8-shorter}, |
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2364 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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2365 topics={LongTerm},cat={C}, |
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2366 } |
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2367 |
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2368 @ARTICLE{Erhan+al-2010, |
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2369 author = {Erhan, Dumitru and Bengio, Yoshua and Courville, Aaron and Manzagol, Pierre-Antoine and Vincent, Pascal and Bengio, Samy}, |
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2370 title = {Why Does Unsupervised Pre-training Help Deep Learning?}, |
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2371 volume = {11}, |
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2372 year = {2010}, |
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2373 pages = {625--660}, |
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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583
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2374 crossref = {JMLR-shorter}, |
498
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2375 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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2376 } |
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2377 |
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2378 @INPROCEEDINGS{Erhan-aistats-2010, |
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2379 author = {Erhan, Dumitru and Courville, Aaron and Bengio, Yoshua and Vincent, Pascal}, |
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2380 title = {Why Does Unsupervised Pre-training Help Deep Learning?}, |
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2381 booktitle = {Proceedings of AISTATS 2010}, |
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2382 volume = {9}, |
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2383 year = {2010}, |
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2384 pages = {201-208}, |
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2385 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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2386 } |
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2387 |
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2388 @MASTERSTHESIS{Erhan-MSc, |
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2389 author = {Erhan, Dumitru}, |
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2390 keywords = {Apprentisage multit{\^{a}}che, Filtrage collaboratif, M{\'{e}}thodes {\`{a}} noyaux, QSAR, R{\'{e}}seaux de neurones}, |
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2391 title = {Collaborative filtering techniques for drug discovery}, |
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2392 year = {2006}, |
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2393 school = {Universit{\'{e}} de Montr{\'{e}}al}, |
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2394 abstract = {Cette th{\`{e}}se examine le probl{\`{e}}me d'apprendre plusieurs t{\^{a}}ches simultan{\'{e}}ment, |
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2395 afin de transf{\'{e}}rer les connaissances apprises {\`{a}} une nouvelle t{\^{a}}che. Si |
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2396 on suppose que les t{\^{a}}ches partagent une repr{\'{e}}sentation et qu'il est possible de |
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2397 d{\'{e}}couvrir cette repr{\'{e}}sentation efficacement, cela peut nous servir {\`{a}} construire un |
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2398 meilleur mod{\`{e}}le de la nouvelle t{\^{a}}che. Il existe plusieurs variantes de |
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2399 cette m{\'{e}}thode: transfert inductif, apprentisage multit{\^{a}}che, filtrage |
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2400 collaboratif etc. Nous avons {\'{e}}valu{\'{e}} plusieurs algorithmes d'apprentisage |
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2401 supervis{\'{e}} pour d{\'{e}}couvrir des repr{\'{e}}sentations partag{\'{e}}es parmi les |
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2402 t{\^{a}}ches d{\'{e}}finies dans un probl{\`{e}}me de chimie computationelle. Nous avons |
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2403 formul{\'{e}} le probl{\`{e}}me dans un cadre d'apprentisage automatique, |
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2404 fait l'analogie avec les algorithmes standards de filtrage collaboratif et construit les |
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2405 hypoth{\`{e}}ses g{\'{e}}n{\'{e}}rales qui devraient {\^{e}}tre test{\'{e}}es pour valider l'utilitisation des |
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2406 algorithmes multit{\^{a}}che. Nous avons aussi {\'{e}}valu{\'{e}} la performance des algorithmes |
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2407 d'apprentisage utilis{\'{e}}s et d{\'{e}}montrons qu'il est, en effet, possible de trouver une |
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2408 repr{\'{e}}sentation partag{\'{e}}e pour le probl{\`{e}}me consider{\'{e}}. Du point de vue |
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2409 th{\'{e}}orique, notre apport est une modification d'un algorithme |
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2410 standard---les machines {\`{a}} vecteurs de support--qui produit des r{\'{e}}sultats |
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2411 comparables aux meilleurs algorithmes disponsibles et qui utilise {\`{a}} fond les |
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2412 concepts de l'apprentisage multit{\^{a}}che. Du point de vue pratique, notre |
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2413 apport est l'utilisation de notre algorithme par les compagnies |
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2414 pharmaceutiques dans leur d{\'{e}}couverte de nouveaux m{\'{e}}dicaments.} |
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2415 } |
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2416 |
604
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2417 @TECHREPORT{Erhan-vis-techreport-2010, |
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2418 author = {Erhan, Dumitru and Courville, Aaron and Bengio, Yoshua}, |
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2419 title = {Understanding Representations Learned in Deep Architectures}, |
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2420 number = {1355}, |
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2421 year = {2010}, |
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2422 institution = {Universit{\'{e}} de Montr{\'{e}}al/DIRO}, |
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2423 abstract = {Deep architectures have demonstrated state-of-the-art performance in a variety of |
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2424 settings, especially with vision datasets. Deep learning algorithms are based on learning |
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2425 several levels of representation of the input. Beyond test-set performance, there |
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2426 is a need for qualitative comparisons of the solutions learned by various deep architectures, |
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2427 focused on those learned representations. One of the goals of our research |
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2428 is to improve tools for finding good qualitative interpretations of high level features |
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2429 learned by such models. We also seek to gain insight into the invariances learned by |
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2430 deep networks. To this end, we contrast and compare several techniques for finding |
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2431 such interpretations. We applied our techniques on Stacked Denoising Auto-Encoders |
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2432 and Deep Belief Networks, trained on several vision datasets. We show that consistent |
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2433 filter-like interpretation is possible and simple to accomplish at the unit level. The tools |
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2434 developed make it possible to analyze deep models in more depth and accomplish the |
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2435 tracing of invariance manifolds for each of the hidden units. We hope that such techniques |
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2436 will allow researchers in deep architectures to understand more of how and why |
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2437 deep architectures work.} |
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2438 } |
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2439 |
498
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2440 @INPROCEEDINGS{Erhan2009, |
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2441 author = {Erhan, Dumitru and Manzagol, Pierre-Antoine and Bengio, Yoshua and Bengio, Samy and Vincent, Pascal}, |
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2442 keywords = {Deep Networks}, |
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2443 title = {The Difficulty of Training Deep Architectures and the effect of Unsupervised Pre-Training}, |
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2444 year = {2009}, |
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2445 pages = {153--160}, |
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2446 crossref = {xAISTATS2009-shorter}, |
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2447 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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2448 } |
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2449 |
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2450 @ARTICLE{gasser+eck+port:1999, |
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2451 author = {Gasser, M. and Eck, Douglas and Port, Robert}, |
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2452 title = {Meter as Mechanism: A Neural Network Model that Learns Metrical patterns}, |
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2453 journal = {Connection Science}, |
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2454 volume = {11}, |
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2455 number = {2}, |
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2456 year = {1999}, |
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2457 pages = {187--216}, |
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2458 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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2459 own={Have}, |
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2460 source={OwnPublication}, |
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2461 sourcetype={Journal}, |
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2462 } |
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2463 |
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2464 @TECHREPORT{gasser+eck+port:tr-1996, |
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2465 author = {Gasser, M. and Eck, Douglas and Port, Robert}, |
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2466 title = {Meter as Mechanism A Neural Network that Learns Metrical Patterns}, |
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2467 number = {180}, |
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2468 year = {1996}, |
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2469 institution = {Indiana University Cognitive Science Program}, |
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2470 source={OwnPublication}, |
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2471 sourcetype={TechReport}, |
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2472 } |
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2473 |
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2474 @INPROCEEDINGS{gasser+eck:1996, |
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2475 author = {Gasser, M. and Eck, Douglas}, |
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2476 editor = {}, |
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2477 title = {Representing Rhythmic Patterns in a Network of Oscillators}, |
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2478 booktitle = {{The Proceedings of the International Conference on Music Perception and Cognition}}, |
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2479 number = {4}, |
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2480 year = {1996}, |
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2481 pages = {361--366}, |
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2482 publisher = {Lawrence Erlbaum Associates}, |
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2483 url = {http://www.iro.umontreal.ca/~eckdoug/papers/1996_gasser_icmpc.pdf}, |
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2484 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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2485 source={OwnPublication}, |
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2486 sourcetype={Conference}, |
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2487 } |
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2488 |
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2489 @INPROCEEDINGS{gers+eck+schmidhuber:icann2001, |
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2490 author = {Gers, F. A. and Eck, Douglas and Schmidhuber, Juergen}, |
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2491 editor = {Dorffner, Georg}, |
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2492 title = {Applying {LSTM} to Time Series Predictable Through Time-Window Approaches}, |
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2493 booktitle = {{Artificial Neural Networks -- ICANN 2001 (Proceedings)}}, |
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2494 year = {2001}, |
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2495 pages = {669--676}, |
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2496 publisher = {Springer}, |
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2497 url = {http://www.iro.umontreal.ca/~eckdoug/papers/2001_gers_icann.pdf}, |
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2498 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.}, |
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2499 source={OwnPublication}, |
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2500 sourcetype={Conference}, |
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2501 } |
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2502 |
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2503 @TECHREPORT{gers+eck+schmidhuber:tr-2000, |
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2504 author = {Gers, F. A. and Eck, Douglas and Schmidhuber, Juergen}, |
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2505 title = {Applying {LSTM} to Time Series Predictable Through Time-Window Approaches}, |
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2506 number = {IDSIA-22-00}, |
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2507 year = {2000}, |
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2508 institution = {IDSIA}, |
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2509 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.}}, |
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2510 ps={ftp://ftp.idsia.ch/pub/techrep/IDSIA-22-00.ps.gz}, |
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2511 source={OwnPublication}, |
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2512 sourcetype={TechReport}, |
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2513 } |
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2514 |
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2515 @INPROCEEDINGS{gers+perez+eck+schmidhuber:esann2002, |
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2516 author = {Gers, F. A. and Perez-Ortiz, J. A. and Eck, Douglas and Schmidhuber, Juergen}, |
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2517 title = {{DEKF-LSTM}}, |
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2518 booktitle = {Proceedings of the 10th European Symposium on Artificial Neural Networks, ESANN 2002}, |
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2519 year = {2002}, |
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2520 source={OwnPublication}, |
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2521 sourcetype={Conference}, |
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2522 } |
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2523 |
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2524 @INPROCEEDINGS{gers+perez+eck+schmidhuber:icannA2002, |
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2525 author = {Gers, F. A. and Perez-Ortiz, J. A. and Eck, Douglas and Schmidhuber, Juergen}, |
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2526 editor = {Dorronsoro, J.}, |
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2527 title = {Learning Context Sensitive Languages with {LSTM} Trained with {Kalman} Filters}, |
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2528 booktitle = {{Artificial Neural Networks -- ICANN 2002 (Proceedings)}}, |
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2529 year = {2002}, |
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2530 pages = {655--660}, |
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2531 publisher = {Springer}, |
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2532 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.}, |
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2533 source={OwnPublication}, |
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2534 sourcetype={Conference}, |
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2535 } |
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2536 |
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2537 @PHDTHESIS{Ghosn-Phd-2003, |
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2538 author = {Ghosn, Joumana}, |
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2539 title = {Apprentissage multi-t{\^{a}}ches et partage de connaissances}, |
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2540 year = {2003}, |
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2541 school = {Universit{\'{e}} de Montr{\'{e}}al} |
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2542 } |
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2543 |
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2544 @INPROCEEDINGS{ghosn97, |
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2545 author = {Ghosn, Joumana and Bengio, Yoshua}, |
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2546 title = {Multi-Task Learning for Stock Selection}, |
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2547 year = {1997}, |
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2548 pages = {946--952}, |
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2549 publisher = {MIT Press, Cambridge, MA}, |
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2550 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/multitask-nips97.pdf}, |
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2551 crossref = {NIPS9}, |
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2552 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.}, |
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2553 topics={MultiTask,Finance},cat={C}, |
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2554 } |
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2555 |
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2556 @TECHREPORT{Gingras-asynchronous-TR96, |
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2557 author = {Gingras, Fran{\c c}ois and Bengio, Yoshua}, |
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2558 title = {Handling asynchronous or missing financial data with recurrent networks}, |
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2559 number = {1020}, |
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2560 year = {1996}, |
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2561 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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2562 topics={Finance,Missing},cat={T}, |
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2563 } |
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2564 |
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2565 @TECHREPORT{Gingras-financial-TR99, |
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2566 author = {Gingras, Fran{\c c}ois and Bengio, Yoshua and Nadeau, Claude}, |
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2567 title = {On Out-of-Sample Statistics for Financial Time-Series}, |
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2568 number = {2585}, |
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2569 year = {1999}, |
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2570 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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2571 topics={Comparative,Finance},cat={T}, |
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2572 } |
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2573 |
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2574 @INPROCEEDINGS{gingras2000, |
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2575 author = {Gingras, Fran{\c c}ois and Bengio, Yoshua and Nadeau, Claude}, |
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2576 title = {On Out-of-Sample Statistics for Time-Series}, |
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2577 booktitle = {Computational Finance 2000}, |
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2578 year = {2000}, |
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2579 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/out-err-cf2000.pdf}, |
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2580 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.}, |
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2581 topics={Comparative,Finance},cat={C}, |
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2582 } |
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2583 |
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2584 @INPROCEEDINGS{GlorotAISTATS2010, |
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2585 author = {Bengio, Yoshua and Glorot, Xavier}, |
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2586 title = {Understanding the difficulty of training deep feedforward neural networks}, |
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2587 booktitle = {Proceedings of AISTATS 2010}, |
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2588 volume = {9}, |
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2589 year = {2010}, |
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2590 pages = {249-256}, |
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2591 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.} |
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2592 } |
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2593 |
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2594 @INPROCEEDINGS{Gori89, |
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2595 author = {Gori, Marco and Bengio, Yoshua and De Mori, Renato}, |
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2596 title = {BPS: a learning algorithm for capturing the dynamic nature of speech}, |
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2597 booktitle = {International Joint Conference on Neural Networks}, |
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2598 volume = {2}, |
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2599 year = {1989}, |
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2600 pages = {417--424}, |
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2601 publisher = {IEEE, New York}, |
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2602 topics={Speech},cat={C}, |
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2603 } |
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2604 |
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2605 @INCOLLECTION{Grandvalet+Bengio-ssl-2006, |
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2606 author = {Grandvalet, Yves and Bengio, Yoshua}, |
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2607 editor = {Chapelle, Olivier and {Sch{\"{o}}lkopf}, Bernhard and Zien, Alexander}, |
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2608 title = {Entropy Regularization}, |
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2609 booktitle = {Semi-Supervised Learning}, |
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2610 year = {2006}, |
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2611 pages = {151--168}, |
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2612 publisher = {{MIT} Press}, |
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2613 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/entropy_regularization_2006.pdf}, |
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2614 abstract = {The problem of semi-supervised induction consists in learning a decision rule from |
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2615 labeled and unlabeled data. This task can be undertaken by discriminative methods, |
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2616 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 |
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2617 the framework of maximum a posteriori estimation. The learning criterion is derived |
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2618 from clearly stated assumptions and can be applied to any smoothly parametrized |
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2619 model of posterior probabilities. The regularization scheme favors low density separation, without any modeling of the density of input features. The contribution |
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2620 of unlabeled data to the learning criterion induces local optima, but this problem |
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2621 can be alleviated by deterministic annealing. For well-behaved models of posterior |
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2622 probabilities, deterministic annealing {EM} provides a decomposition of the learning |
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2623 problem in a series of concave subproblems. Other approaches to the semi-supervised |
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2624 problem are shown to be close relatives or limiting cases of entropy regularization. |
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2625 A series of experiments illustrates the good behavior of the algorithm in terms of |
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2626 performance and robustness with respect to the violation of the postulated low density separation assumption. The minimum entropy solution benefits from unlabeled |
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2627 data and is able to challenge mixture models and manifold learning in a number of |
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2628 situations.}, |
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2629 cat={B},topics={Unsupervised}, |
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2630 } |
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2631 |
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2632 @INPROCEEDINGS{graves+eck+schmidhuber:bio-adit2004, |
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2633 author = {Graves, A. and Eck, Douglas and Beringer, N. and Schmidhuber, Juergen}, |
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2634 title = {Biologically Plausible Speech Recognition with {LSTM} Neural Nets}, |
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2635 booktitle = {Proceedings of the First Int'l Workshop on Biologically Inspired Approaches to Advanced Information Technology (Bio-ADIT)}, |
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2636 year = {2004}, |
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2637 pages = {127-136}, |
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2638 url = {http://www.iro.umontreal.ca/~eckdoug/papers/2004_bioadit.pdf}, |
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2639 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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2640 source={OwnPublication}, |
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2641 sourcetype={Conference}, |
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2642 } |
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2643 |
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2644 @TECHREPORT{graves+eck+schmidhuber:tr-digits2003, |
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2645 author = {Graves, A. and Eck, Douglas and Schmidhuber, Juergen}, |
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2646 title = {Comparing {LSTM} Recurrent Networks and Spiking Recurrent Networks on the Recognition of Spoken Digits}, |
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2647 number = {IDSIA-13-03}, |
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2648 year = {2003}, |
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2649 institution = {IDSIA}, |
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2650 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.}, |
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2651 source={OwnPublication}, |
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2652 sourcetype={TechReport}, |
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2653 } |
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2654 |
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2655 @INPROCEEDINGS{haffner-98, |
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2656 author = {Haffner, Patrick and Bottou, {L{\'{e}}on} and G. Howard, Paul and Simard, Patrice and Bengio, Yoshua and {LeCun}, Yann}, |
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2657 title = {Browsing through High Quality Document Images with {DjVu}}, |
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2658 booktitle = {Proc. of Advances in Digital Libraries 98}, |
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2659 year = {1998}, |
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2660 pages = {309--318}, |
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2661 publisher = {IEEE}, |
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2662 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/haffner-98.ps.gz}, |
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2663 topics={HighDimensional},cat={C}, |
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2664 } |
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2665 |
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2666 @INPROCEEDINGS{Hamel+al-2009, |
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2667 author = {Hamel, Philippe and Wood, Sean and Eck, Douglas}, |
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2668 title = {Automatic Identification of Instrument Classes in Polyphonic and Poly-Instrument Audio}, |
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2669 booktitle = {10th International Society for Music Information Retrieval Conference}, |
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2670 year = {2009}, |
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2671 pages = {399--404}, |
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2672 url = {http://ismir2009.ismir.net/proceedings/PS3-2.pdf}, |
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2673 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.} |
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2674 } |
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2675 |
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2676 @MISC{Hugo+al-snowbird-2007, |
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2677 author = {Larochelle, Hugo and Bengio, Yoshua and Erhan, Dumitru}, |
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2678 title = {Generalization to a zero-data task: an empirical study}, |
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2679 year = {2007}, |
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2680 howpublished = {Talk and poster presented at the Learning Workshop(Snowbird), San Juan, Puerto Rico, 2007} |
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2681 } |
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2682 |
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2683 @INPROCEEDINGS{hyper:2000:ijcnn, |
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2684 author = {Bengio, Yoshua}, |
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2685 title = {Continuous Optimization of Hyper-Parameters}, |
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2686 booktitle = {International Joint Conference on Neural Networks 2000}, |
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2687 volume = {I}, |
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2688 year = {2000}, |
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2689 pages = {305--310}, |
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2690 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/hyper-ijcnn2000.pdf}, |
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2691 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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2692 topics={ModelSelection},cat={C}, |
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2693 } |
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2694 |
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2695 @INPROCEEDINGS{ICML01, |
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2696 editor = {Brodley, Carla E. and Danyluk, Andrea Pohoreckyj}, |
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2697 title = {Proceedings of the Eighteenth International Conference on Machine Learning (ICML'01)}, |
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2698 booktitle = {Proceedings of the Eighteenth International Conference on Machine Learning (ICML'01)}, |
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2699 year = {-1}, |
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2700 publisher = {Morgan Kaufmann} |
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2701 } |
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2702 |
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2703 @INPROCEEDINGS{ICML01-short, |
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2704 editor = {Brodley, Carla E. and Danyluk, Andrea Pohoreckyj}, |
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2705 title = {Proceedings of the Eighteenth International Conference on Machine Learning (ICML'01)}, |
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2706 booktitle = {ICML'01}, |
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2707 year = {-1}, |
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2708 publisher = {Morgan Kaufmann} |
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2709 } |
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2710 |
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2711 |
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2712 @INPROCEEDINGS{ICML02, |
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2713 editor = {Sammut, Claude and Hoffmann, Achim G.}, |
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2714 title = {Proceedings of the Nineteenth International Conference on Machine Learning (ICML'02)}, |
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2715 booktitle = {Proceedings of the Nineteenth International Conference on Machine Learning (ICML'02)}, |
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2716 year = {-1}, |
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2717 publisher = {Morgan Kaufmann} |
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2718 } |
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2719 |
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2720 @INPROCEEDINGS{ICML02-short, |
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2721 editor = {Sammut, Claude and Hoffmann, Achim G.}, |
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|
2722 title = {Proceedings of the Nineteenth International Conference on Machine Learning (ICML'02)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2723 booktitle = {ICML'02}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2724 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2725 publisher = {Morgan Kaufmann} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2726 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2727 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2728 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2729 @INPROCEEDINGS{ICML03, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2730 editor = {Fawcett, Tom and Mishra, Nina}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2731 title = {Proceedings of the Twenty International Conference on Machine Learning (ICML'03)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2732 booktitle = {Proceedings of the Twenty International Conference on Machine Learning (ICML'03)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2733 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2734 publisher = {AAAI Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2735 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2736 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2737 @INPROCEEDINGS{ICML03-short, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2738 editor = {Fawcett, Tom and Mishra, Nina}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2739 title = {Proceedings of the Twenty International Conference on Machine Learning (ICML'03)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2740 booktitle = {ICML'03}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2741 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2742 publisher = {AAAI Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2743 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2744 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2745 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2746 @INPROCEEDINGS{ICML04, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2747 editor = {Brodley, Carla E.}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2748 title = {Proceedings of the Twenty-first International Conference on Machine Learning (ICML'04)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2749 booktitle = {Proceedings of the Twenty-first International Conference on Machine Learning (ICML'04)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2750 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2751 publisher = {ACM} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2752 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2753 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2754 @INPROCEEDINGS{ICML04-short, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2755 editor = {Brodley, Carla E.}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2756 title = {Proceedings of the Twenty-first International Conference on Machine Learning (ICML'04)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2757 booktitle = {ICML'04}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2758 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2759 publisher = {ACM} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2760 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2761 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2762 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2763 @INPROCEEDINGS{ICML05-short, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2764 editor = {Raedt, Luc De and Wrobel, Stefan}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2765 title = {Proceedings of the Twenty-second International Conference on Machine Learning (ICML'05)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2766 booktitle = {ICML'05}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2767 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2768 publisher = {ACM} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2769 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2770 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2771 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2772 @INPROCEEDINGS{ICML06-short, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2773 editor = {Cohen, William W. and Moore, Andrew}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2774 title = {Proceedings of the Twenty-three International Conference on Machine Learning (ICML'06)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2775 booktitle = {ICML'06}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2776 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2777 publisher = {ACM} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2778 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2779 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2780 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2781 @INPROCEEDINGS{ICML07-short, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2782 editor = {Ghahramani, Zoubin}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2783 title = {Proceedings of the 24th International Conference on Machine Learning (ICML'07)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2784 booktitle = {ICML'07}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2785 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2786 publisher = {ACM} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2787 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2788 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2789 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2790 @INPROCEEDINGS{ICML08-short, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2791 editor = {Cohen, William W. and McCallum, Andrew and Roweis, Sam T.}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2792 title = {Proceedings of the Twenty-fifth International Conference on Machine Learning (ICML'08)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2793 booktitle = {ICML'08}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2794 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2795 publisher = {ACM} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2796 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2797 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2798 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2799 @INPROCEEDINGS{ICML09-short, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2800 editor = {Bottou, {L{\'{e}}on} and Littman, Michael}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2801 title = {Proceedings of the Twenty-sixth International Conference on Machine Learning (ICML'09)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2802 booktitle = {ICML'09}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2803 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2804 publisher = {ACM} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2805 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2806 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2807 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2808 @INPROCEEDINGS{ICML96, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2809 editor = {Saitta, L.}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2810 title = {Proceedings of the Thirteenth International Conference on Machine Learning (ICML'96)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2811 booktitle = {Proceedings of the Thirteenth International Conference on Machine Learning (ICML'96)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2812 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2813 publisher = {Morgan Kaufmann} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2814 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2815 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2816 @INPROCEEDINGS{ICML96-short, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2817 editor = {Saitta, L.}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2818 title = {Proceedings of the Thirteenth International Conference on Machine Learning (ICML'96)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2819 booktitle = {ICML'96}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2820 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2821 publisher = {Morgan Kaufmann} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2822 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2823 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2824 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2825 @INPROCEEDINGS{ICML97, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2826 editor = {Fisher, Douglas H.}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2827 title = {{}Proceedings of the Fourteenth International Conference on Machine Learning (ICML'97)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2828 booktitle = {Proceedings of the Fourteenth International Conference on Machine Learning (ICML'97)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2829 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2830 publisher = {Morgan Kaufmann} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2831 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2832 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2833 @INPROCEEDINGS{ICML97-short, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2834 editor = {Fisher, Douglas H.}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2835 title = {{}Proceedings of the Fourteenth International Conference on Machine Learning (ICML'97)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2836 booktitle = {ICML'97}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2837 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2838 publisher = {Morgan Kaufmann} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2839 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2840 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2841 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2842 @INPROCEEDINGS{ICML98, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
2843 editor = {Shavlik, Jude W.}, |
7ff00c27c976
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2844 title = {Proceedings of the Fifteenth International Conference on Machine Learning (ICML'98)}, |
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2845 booktitle = {Proceedings of the Fifteenth International Conference on Machine Learning (ICML'98)}, |
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2846 year = {-1}, |
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2847 publisher = {Morgan Kaufmann} |
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2848 } |
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2849 |
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2850 @INPROCEEDINGS{ICML98-short, |
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2851 editor = {Shavlik, Jude W.}, |
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2852 title = {Proceedings of the Fifteenth International Conference on Machine Learning (ICML'98)}, |
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2853 booktitle = {ICML'98}, |
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2854 year = {-1}, |
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2855 publisher = {Morgan Kaufmann} |
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2856 } |
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2857 |
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2858 |
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2859 @INPROCEEDINGS{ICML99, |
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2860 editor = {Bratko, Ivan and Dzeroski, Saso}, |
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2861 title = {Proceedings of the Sixteenth International Conference on Machine Learning (ICML'99)}, |
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2862 booktitle = {Proceedings of the Sixteenth International Conference on Machine Learning (ICML'99)}, |
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2863 year = {-1}, |
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2864 publisher = {Morgan Kaufmann} |
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2865 } |
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Frederic Bastien <nouiz@nouiz.org>
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2866 |
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2867 @INPROCEEDINGS{ICML99-short, |
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2868 editor = {Bratko, Ivan and Dzeroski, Saso}, |
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Frederic Bastien <nouiz@nouiz.org>
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2869 title = {Proceedings of the Sixteenth International Conference on Machine Learning (ICML'99)}, |
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2870 booktitle = {ICML'99}, |
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Frederic Bastien <nouiz@nouiz.org>
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2871 year = {-1}, |
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2872 publisher = {Morgan Kaufmann} |
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2873 } |
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2874 |
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Frederic Bastien <nouiz@nouiz.org>
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2875 |
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2876 @INCOLLECTION{jaeger+eck:2007, |
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Frederic Bastien <nouiz@nouiz.org>
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2877 author = {Jaeger, H. and Eck, Douglas}, |
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Frederic Bastien <nouiz@nouiz.org>
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2878 title = {Can't get you out of my head: {A} connectionist model of cyclic rehearsal}, |
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Frederic Bastien <nouiz@nouiz.org>
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2879 booktitle = {Modeling Communications with Robots and Virtual Humans}, |
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Frederic Bastien <nouiz@nouiz.org>
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2880 series = {{LNCS}}, |
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Frederic Bastien <nouiz@nouiz.org>
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2881 year = {2007}, |
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Frederic Bastien <nouiz@nouiz.org>
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2882 publisher = {Springer-Verlag}, |
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Frederic Bastien <nouiz@nouiz.org>
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2883 url = {http://www.iro.umontreal.ca/~eckdoug/papers/2007_jaeger_eck.pdf}, |
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2884 source={OwnPublication}, |
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2885 sourcetype={Chapter}, |
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|
2886 } |
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Frederic Bastien <nouiz@nouiz.org>
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|
2887 |
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Frederic Bastien <nouiz@nouiz.org>
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2888 @MISC{James+al-snowbird-2008, |
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Frederic Bastien <nouiz@nouiz.org>
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2889 author = {Bergstra, James and Bengio, Yoshua and Louradour, Jerome}, |
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Frederic Bastien <nouiz@nouiz.org>
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2890 title = {Image Classification using Higher-Order Neural Models}, |
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Frederic Bastien <nouiz@nouiz.org>
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2891 year = {2008}, |
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Frederic Bastien <nouiz@nouiz.org>
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|
2892 howpublished = {The Learning Workshop (Snowbird, Utah)}, |
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Frederic Bastien <nouiz@nouiz.org>
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2893 url = {http://snowbird.djvuzone.org/2007/abstracts/161.pdf} |
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2894 } |
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2895 |
604
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
583
diff
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|
2896 @ARTICLE{JMLR-short, |
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
583
diff
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2897 journal = {JMLR}, |
51213beaed8b
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
583
diff
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|
2898 year = {-1} |
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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583
diff
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|
2899 } |
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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583
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2900 |
498
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2901 |
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2902 @INPROCEEDINGS{Kegl+Bertin+Eck-2008, |
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Frederic Bastien <nouiz@nouiz.org>
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2903 author = {K{\'{e}}gl, Bal{\'{a}}zs and Bertin-Mahieux, Thierry and Eck, Douglas}, |
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2904 title = {Metropolis-Hastings Sampling in a FilterBoost Music Classifier}, |
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2905 booktitle = {Music and machine learning workshop (ICML08)}, |
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2906 year = {2008} |
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2907 } |
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2908 |
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2909 @INPROCEEDINGS{kegl2005b, |
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Frederic Bastien <nouiz@nouiz.org>
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2910 author = {K{\'{e}}gl, Bal{\'{a}}zs}, |
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2911 title = {Generalization Error and Algorithmic Convergence of Median Boosting.}, |
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2912 year = {2005}, |
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Frederic Bastien <nouiz@nouiz.org>
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|
2913 crossref = {NIPS17-shorter}, |
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2914 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.} |
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2915 } |
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2916 |
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2917 @ARTICLE{lacoste+eck:eurasip, |
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2918 author = {Lacoste, Alexandre and Eck, Douglas}, |
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2919 title = {A Supervised Classification Algorithm For Note Onset Detection}, |
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2920 journal = {EURASIP Journal on Applied Signal Processing}, |
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Frederic Bastien <nouiz@nouiz.org>
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2921 volume = {2007}, |
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Frederic Bastien <nouiz@nouiz.org>
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|
2922 number = {ID 43745}, |
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|
2923 year = {2007}, |
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Frederic Bastien <nouiz@nouiz.org>
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|
2924 pages = {1--13}, |
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|
2925 source={OwnPublication}, |
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|
2926 sourcetype={Journal}, |
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2927 } |
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Frederic Bastien <nouiz@nouiz.org>
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|
2928 |
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2929 @MASTERSTHESIS{Lajoie2009, |
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2930 author = {Lajoie, Isabelle}, |
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|
2931 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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2932 title = {Apprentissage de repr{\'{e}}sentations sur-compl{\`{e}}tes par entra{\^{\i}}nement d’auto-encodeurs}, |
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2933 year = {2009}, |
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Frederic Bastien <nouiz@nouiz.org>
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|
2934 school = {Universit{\'{e}} de Montr{\'{e}}al}, |
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|
2935 abstract = {Les avanc{\'{e}}s dans le domaine de l’intelligence artificielle, permettent {\`{a}} des syst{\`{e}}mes |
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|
2936 informatiques de r{\'{e}}soudre des t{\^{a}}ches de plus en plus complexes li{\'{e}}es par exemple {\`{a}} |
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2937 la vision, {\`{a}} la compr{\'{e}}hension de signaux sonores ou au traitement de la langue. Parmi |
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2938 les mod{\`{e}}les existants, on retrouve les R{\'{e}}seaux de Neurones Artificiels (RNA), dont la |
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2939 popularit{\'{e}} a fait un grand bond en avant avec la d{\'{e}}couverte de Hinton et al. [22], soit |
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2940 l’utilisation de Machines de {Boltzmann} Restreintes (RBM) pour un pr{\'{e}}-entra{\^{\i}}nement |
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Frederic Bastien <nouiz@nouiz.org>
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2941 non-supervis{\'{e}} couche apr{\`{e}}s couche, facilitant grandement l’entra{\^{\i}}nement supervis{\'{e}} du |
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Frederic Bastien <nouiz@nouiz.org>
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|
2942 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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|
2943 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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2944 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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2945 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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2946 avons approfondi notre compr{\'{e}}hension du pr{\'{e}}-entra{\^{\i}}nement de type SDAE, explor{\'{e}} ses |
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Frederic Bastien <nouiz@nouiz.org>
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|
2947 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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Frederic Bastien <nouiz@nouiz.org>
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|
2948 d’architecture profonde. Nous avons ainsi pu, entre autres choses, mettre en lumi{\`{e}}re |
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|
2949 l’influence du niveau de bruit, du nombre de couches et du nombre d’unit{\'{e}}s cach{\'{e}}es |
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Frederic Bastien <nouiz@nouiz.org>
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|
2950 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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2951 performance sur la t{\^{a}}che supervis{\'{e}}e avec l’utilisation des bruits poivre et sel (PS) et |
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2952 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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2953 masque {\`{a}} z{\'{e}}ro (MN). De plus, nous avons d{\'{e}}montr{\'{e}} que la performance profitait d’une |
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2954 emphase impos{\'{e}}e sur la reconstruction des donn{\'{e}}es corrompues durant l’entra{\^{\i}}nement |
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2955 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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2956 les cellules V1 du cortex visuel, soit des filtres d{\'{e}}tecteurs de bordures. Nous aurons par |
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2957 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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2958 support ({SVM}) lin{\'{e}}aire ou {\`{a}} noyau gaussien, am{\'{e}}liorant grandement sa performance de |
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2959 g{\'{e}}n{\'{e}}ralisation. Aussi, nous aurons observ{\'{e}} que similairement au DBN, et contrairement |
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2960 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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2961 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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2962 (SRAE).} |
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2963 } |
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2964 |
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2965 @INPROCEEDINGS{lamere+eck:ismir2007, |
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2966 author = {Lamere, Paul and Eck, Douglas}, |
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2967 editor = {}, |
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2968 title = {Using 3D Visualizations to Explore and Discover Music}, |
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2969 booktitle = {{Proceedings of the 8th International Conference on Music Information Retrieval ({ISMIR} 2007)}}, |
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2970 year = {2007}, |
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2971 publisher = {}, |
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2972 source={OwnPublication}, |
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2973 sourcetype={Conference}, |
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2974 } |
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2975 |
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2976 @ARTICLE{Larochelle+al-2010, |
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2977 author = {Larochelle, Hugo and Bengio, Yoshua and Turian, Joseph}, |
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2978 title = {Tractable Multivariate Binary Density Estimation and the Restricted {Boltzmann} Forest}, |
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2979 journal = {Neural Computation}, |
604
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2980 volume = {22}, |
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2981 number = {9}, |
498
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2982 year = {2010}, |
604
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2983 pages = {2285--2307} |
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2984 } |
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2985 |
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2986 @INPROCEEDINGS{Larochelle+Bengio-2008, |
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2987 author = {Larochelle, Hugo and Bengio, Yoshua}, |
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2988 title = {Classification using Discriminative Restricted {B}oltzmann Machines}, |
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2989 year = {2008}, |
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2990 pages = {536--543}, |
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2991 crossref = {ICML08-shorter}, |
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2992 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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2993 for deep feed-forward neural network classifiers, and are not considered as a standalone solution to classification problems. In |
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2994 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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2995 RBMs which aim at introducing a discriminative component to RBM training and improve their performance as classifiers. This |
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2996 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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2997 } |
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2998 |
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2999 @INPROCEEDINGS{Larochelle-2009, |
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3000 author = {Larochelle, Hugo and Erhan, Dumitru and Vincent, Pascal}, |
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3001 title = {Deep Learning using Robust Interdependent Codes}, |
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3002 booktitle = {Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS 2009)}, |
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3003 year = {2009}, |
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3004 pages = {312--319}, |
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3005 date = "April 16-18, 2009", |
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3006 } |
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3007 |
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3008 @ARTICLE{Larochelle-jmlr-2009, |
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3009 author = {Larochelle, Hugo and Bengio, Yoshua and Louradour, Jerome and Lamblin, Pascal}, |
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3010 title = {Exploring Strategies for Training Deep Neural Networks}, |
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3011 volume = {10}, |
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3012 year = {2009}, |
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3013 pages = {1--40}, |
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3014 crossref = {JMLR-shorter}, |
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3015 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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3016 } |
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3017 |
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3018 @PHDTHESIS{Larochelle-PhD-2009, |
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3019 author = {Larochelle, Hugo}, |
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3020 keywords = {apprentissage non-supervis{\'{e}}, architecture profonde, autoassociateur, autoencodeur, machine de {Boltzmann} restreinte, r{\'{e}}seau de neurones artificiel}, |
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3021 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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3022 year = {2009}, |
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3023 school = {University of Montr{\'{e}}al}, |
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3024 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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3025 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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3026 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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3027 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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3028 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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3029 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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3030 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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3031 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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3032 forme d'apprentissage non-supervis{\'{e}}. |
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3033 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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3034 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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3035 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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3036 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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3037 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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3038 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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3039 {\^{e}}tre apprises et sont b{\'{e}}n{\'{e}}fiques {\`{a}} la performance d'un r{\'{e}}seau profond.} |
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3040 } |
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3041 |
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3042 @INPROCEEDINGS{Larochelle2008, |
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3043 author = {Larochelle, Hugo and Erhan, Dumitru and Bengio, Yoshua}, |
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3044 title = {Zero-data Learning of New Tasks}, |
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3045 booktitle = {AAAI Conference on Artificial Intelligence}, |
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3046 year = {2008}, |
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3047 url = {http://www-etud.iro.umontreal.ca/~larocheh/publications/aaai2008_zero-data.pdf}, |
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3048 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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3049 for deep feed-forward neural network classifiers, and are not considered as a standalone solution to classification problems. In |
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3050 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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3051 RBMs which aim at introducing a discriminative component to RBM training and improve their performance as classifiers. This |
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3052 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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3053 } |
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3054 |
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3055 @INPROCEEDINGS{LarochelleH2007, |
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3056 author = {Larochelle, Hugo and Erhan, Dumitru and Courville, Aaron and Bergstra, James and Bengio, Yoshua}, |
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3057 title = {An Empirical Evaluation of Deep Architectures on Problems with Many Factors of Variation}, |
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3058 year = {2007}, |
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3059 pages = {473--480}, |
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3060 crossref = {ICML07-shorter}, |
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3061 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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3062 } |
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3063 |
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3064 @MASTERSTHESIS{Latendresse-MSc, |
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3065 author = {Latendresse, Simon}, |
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3066 title = {L'utilisation d'hyper-param{\`{e}}tres pour la selection de variables}, |
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3067 year = {1999}, |
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3068 school = {Universit{\'{e}} de Montreal, Dept. IRO}, |
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3069 note = {(in French)} |
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3070 } |
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3071 |
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3072 @MASTERSTHESIS{Lauzon99, |
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3073 author = {Lauzon, Vincent-Philippe}, |
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3074 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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3075 year = {1999}, |
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3076 school = {D{\'{e}}epartement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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3077 crossref = {DIRO} |
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3078 } |
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3079 |
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3080 @INPROCEEDINGS{lecun-93, |
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3081 author = {{LeCun}, Yann and Bengio, Yoshua and Henderson, Donnie and Weisbuch, A. and Weissman, H. and L., Jackel}, |
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3082 title = {On-line handwriting recognition with neural networks: spatial representation versus temporal representation.}, |
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3083 booktitle = {Proc. International Conference on handwriting and drawing.}, |
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3084 year = {1993}, |
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3085 publisher = {Ecole Nationale Superieure des Telecommunications}, |
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3086 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/lecun-93.ps.gz}, |
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3087 topics={PriorKnowledge,Speech},cat={C}, |
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3088 } |
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3089 |
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3090 @INPROCEEDINGS{lecun-99, |
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3091 author = {{LeCun}, Yann and Haffner, Patrick and Bottou, {L{\'{e}}on} and Bengio, Yoshua}, |
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3092 editor = {Forsyth, D.}, |
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3093 title = {Object Recognition with Gradient-Based Learning}, |
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3094 booktitle = {Shape, Contour and Grouping in Computer Vision}, |
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3095 year = {1999}, |
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3096 pages = {319-345}, |
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3097 publisher = {Springer}, |
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3098 url = {orig/lecun-99.ps.gz}, |
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3099 topics={PriorKnowledge,Speech},cat={B}, |
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3100 } |
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3101 |
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3102 @TECHREPORT{lecun-99b, |
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3103 author = {{LeCun}, Yann and Haffner, Patrick and Bottou, {L{\'{e}}on} and Bengio, Yoshua}, |
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3104 title = {Gradient-Based Learning for Object Detection, Segmentation and Recognition}, |
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3105 year = {1999}, |
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3106 institution = {AT\&T Labs}, |
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3107 url = {orig/lecun-99b.ps.gz}, |
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3108 topics={Speech},cat={T}, |
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3109 } |
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3110 |
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3111 @INPROCEEDINGS{lecun-bengio-94, |
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3112 author = {{LeCun}, Yann and Bengio, Yoshua}, |
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3113 title = {Word-level training of a handwritten word recognizer based on convolutional neural networks}, |
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3114 booktitle = {Proc. of the International Conference on Pattern Recognition}, |
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3115 volume = {II}, |
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3116 year = {1994}, |
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3117 pages = {88--92}, |
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3118 publisher = {IEEE}, |
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3119 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/icpr-word.ps}, |
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3120 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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3121 topics={Speech},cat={C}, |
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3122 } |
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3123 |
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3124 @INPROCEEDINGS{lecun-bengio-95a, |
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3125 author = {{LeCun}, Yann and Bengio, Yoshua}, |
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3126 editor = {Arbib, M. A.}, |
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3127 title = {Convolutional Networks for Images, Speech, and Time-Series}, |
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3128 booktitle = {The Handbook of Brain Theory and Neural Networks}, |
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3129 year = {1995}, |
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3130 pages = {255--257}, |
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3131 publisher = {MIT Press}, |
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3132 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/handbook-convo.pdf}, |
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3133 topics={PriorKnowledge,Speech},cat={C}, |
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3134 } |
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3135 |
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3136 @INCOLLECTION{lecun-bengio-95b, |
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3137 author = {{LeCun}, Yann and Bengio, Yoshua}, |
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3138 editor = {Arbib, M. A.}, |
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3139 title = {Pattern Recognition and Neural Networks}, |
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3140 booktitle = {The Handbook of Brain Theory and Neural Networks}, |
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3141 year = {1995}, |
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3142 pages = {711--714}, |
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3143 publisher = {MIT Press}, |
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3144 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/handbook-patrec.pdf}, |
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3145 topics={PriorKnowledge,Speech},cat={B}, |
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3146 } |
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3147 |
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3148 @ARTICLE{LeCun98, |
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3149 author = {{LeCun}, Yann and Bottou, {L{\'{e}}on} and Bengio, Yoshua and Haffner, Patrick}, |
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3150 title = {Gradient-Based Learning Applied to Document Recognition}, |
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3151 journal = {Proceedings of the IEEE}, |
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3152 volume = {86}, |
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3153 number = {11}, |
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3154 year = {1998}, |
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3155 pages = {2278--2324}, |
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3156 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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3157 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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3158 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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3159 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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3160 topics={PriorKnowledge,Speech},cat={C}, |
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3161 } |
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3162 |
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3163 @INPROCEEDINGS{Lecun_icassp97, |
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3164 author = {{LeCun}, Yann and Bottou, {L{\'{e}}on} and Bengio, Yoshua}, |
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3165 title = {Reading Checks with graph transformer networks}, |
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3166 booktitle = {International Conference on Acoustics, Speech and Signal Processing}, |
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3167 volume = {1}, |
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3168 year = {1997}, |
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3169 pages = {151--154}, |
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3170 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/lecun-bottou-bengio-97.ps.gz}, |
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3171 topics={Speech},cat={C}, |
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3172 } |
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3173 |
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3174 @ARTICLE{LeRoux+Bengio-2010, |
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3175 author = {Le Roux, Nicolas and Bengio, Yoshua}, |
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3176 title = {Deep Belief Networks are Compact Universal Approximators}, |
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3177 journal = {Neural Computation}, |
604
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3178 volume = {22}, |
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3179 number = {8}, |
498
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3180 year = {2010}, |
604
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3181 pages = {2192-2207}, |
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3182 issn = {0899-7667}, |
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3183 abstract = {Deep Belief Networks (DBN) are generative models with many layers of hidden causal variables, recently introduced by Hinton et al. (2006), along with a greedy layer-wise unsupervised learning algorithm. Building on Le Roux and Bengio (2008) and Sutskever and Hinton (2008), we show that deep but narrow generative networks do not require more parameters than shallow ones to achieve universal approximation. Exploiting the proof technique, we prove that deep but narrow feed-forward neural networks with sigmoidal units can represent any Boolean expression.} |
498
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3184 } |
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3185 |
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3186 @TECHREPORT{LeRoux-Bengio-2007-TR, |
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3187 author = {Le Roux, Nicolas and Bengio, Yoshua}, |
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3188 title = {Representational Power of Restricted {B}oltzmann Machines and Deep Belief Networks}, |
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3189 number = {1294}, |
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3190 year = {2007}, |
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3191 institution = {D{\'{e}}partement d'Informatique et de Recherche Op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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3192 abstract = {Deep Belief Networks (DBN) are generative neural network models with |
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3193 many layers of hidden explanatory factors, recently introduced by Hinton et al., |
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3194 along with a greedy layer-wise unsupervised learning algorithm. The building |
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3195 block of a DBN is a probabilistic model called a Restricted {Boltzmann} Machine |
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3196 (RBM), used to represent one layer of the model. Restricted {Boltzmann} Machines |
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3197 are interesting because inference is easy in them, and because they have been |
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3198 successfully used as building blocks for training deeper models. |
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3199 We first prove that adding hidden units yields strictly improved modeling |
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3200 power, while a second theorem shows that RBMs are universal approximators of |
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3201 discrete distributions. We then study the question of whether DBNs with more |
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3202 layers are strictly more powerful in terms of representational power. This |
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3203 suggests a new and less greedy criterion for training RBMs within DBNs.} |
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3204 } |
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3205 |
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3206 @ARTICLE{LeRoux-Bengio-2008, |
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3207 author = {Le Roux, Nicolas and Bengio, Yoshua}, |
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3208 title = {Representational Power of Restricted {B}oltzmann Machines and Deep Belief Networks}, |
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3209 journal = {Neural Computation}, |
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3210 volume = {20}, |
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3211 number = {6}, |
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3212 year = {2008}, |
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3213 pages = {1631--1649}, |
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3214 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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3215 } |
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3216 |
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3217 @INPROCEEDINGS{LeRoux-continuous, |
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3218 author = {Le Roux, Nicolas and Bengio, Yoshua}, |
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3219 title = {Continuous Neural Networks}, |
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3220 booktitle = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS'07)}, |
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3221 year = {2007}, |
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3222 publisher = {Omnipress}, |
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3223 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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3224 } |
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3225 |
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3226 @PHDTHESIS{LeRoux-PhD-2008, |
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3227 author = {Le Roux, Nicolas}, |
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3228 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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3229 year = {2008}, |
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3230 school = {Universit{\'{e}} de Montr{\'{e}}al}, |
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3231 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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3232 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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3233 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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3234 {\`{a}} noyau, entre autres). |
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3235 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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3236 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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3237 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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3238 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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3239 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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3240 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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3241 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. |
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3242 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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3243 } |
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3244 |
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3245 @ARTICLE{lheureux-04, |
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3246 author = {{L'Heureux}, Pierre-Jean and Carreau, Julie and Bengio, Yoshua and Delalleau, Olivier and Yue, Shi Yi}, |
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3247 title = {Locally Linear Embedding for dimensionality reduction in {QSAR}}, |
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3248 journal = {Journal of Computer-Aided Molecular Design}, |
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3249 volume = {18}, |
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3250 year = {2004}, |
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3251 pages = {475--482}, |
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3252 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 |
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3253 reduction algorithms, and to be capable of capturing non-linearity in chemical data.}, |
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3254 topics={Bioinformatic},cat={J}, |
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3255 } |
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3256 |
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3257 @TECHREPORT{lm-TR00, |
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3258 author = {Bengio, Yoshua and Ducharme, R{\'{e}}jean and Vincent, Pascal}, |
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3259 title = {A Neural Probabilistic Language Model}, |
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3260 number = {1178}, |
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3261 year = {2000}, |
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3262 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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3263 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/TR1178.pdf}, |
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3264 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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3265 topics={Markov,Unsupervised,Language},cat={T}, |
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3266 } |
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3267 |
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3268 @INPROCEEDINGS{Maillet+al-2009, |
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3269 author = {Maillet, Fran{\c c}ois and Eck, Douglas and Desjardins, Guillaume and Lamere, Paul}, |
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3270 title = {Steerable Playlist Generation by Learning Song Similarity from Radio Station Playlists}, |
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3271 booktitle = {Proceedings of the 10th International Conference on Music Information Retrieval}, |
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3272 year = {2009}, |
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3273 url = {http://www-etud.iro.umontreal.ca/~mailletf/papers/ismir09-playlist.pdf}, |
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3274 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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3275 } |
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3276 |
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3277 @INPROCEEDINGS{manzagol+bertinmahieux+eck:ismir2008, |
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3278 author = {Manzagol, Pierre-Antoine and Bertin-Mahieux, Thierry and Eck, Douglas}, |
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3279 title = {On the Use of Sparse Time-Relative Auditory Codes for Music}, |
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3280 booktitle = {{Proceedings of the 9th International Conference on Music Information Retrieval ({ISMIR} 2008)}}, |
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3281 year = {2008}, |
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3282 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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3283 source={OwnPublication}, |
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3284 sourcetype={Conference}, |
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3285 } |
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3286 |
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3287 @MASTERSTHESIS{Manzagol-Msc-2007, |
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3288 author = {Manzagol, Pierre-Antoine}, |
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3289 key = {Algorithme d'apprentissage, méthode de second ordre, gradient naturel, approximation stochastique}, |
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3290 title = {TONGA - Un algorithme de gradient naturel pour les probl{\`{e}}mes de grande taille}, |
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3291 year = {2007}, |
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3292 school = {Universit{\'{e}} de Montr{\'{e}}al}, |
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3293 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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3294 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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3295 } |
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3296 |
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3297 @INPROCEEDINGS{matic-94, |
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3298 author = {Matic, N. and Henderson, Donnie and {LeCun}, Yann and Bengio, Yoshua}, |
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3299 title = {Pen-based visitor registration system (PENGUIN)}, |
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3300 booktitle = {Conference Record of the Twenty-Eighth Asilomar Conference on Signals, Systems and Computers}, |
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3301 year = {1994}, |
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3302 publisher = {IEEE}, |
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3303 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/matic-94.tiff}, |
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3304 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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3305 topics={Speech},cat={C}, |
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3306 } |
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3307 |
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3308 @UNPUBLISHED{mirex2005artist, |
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3309 author = {Bergstra, James and Casagrande, Norman and Eck, Douglas}, |
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3310 title = {Artist Recognition: A Timbre- and Rhythm-Based Multiresolution Approach}, |
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3311 year = {2005}, |
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3312 note = {{MIREX} artist recognition contest}, |
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3313 source={OwnPublication}, |
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3314 sourcetype={Other}, |
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3315 } |
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3316 |
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3317 @UNPUBLISHED{mirex2005genre, |
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3318 author = {Bergstra, James and Casagrande, Norman and Eck, Douglas}, |
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3319 title = {Genre Classification: Timbre- and Rhythm-Based Multiresolution Audio Classification}, |
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3320 year = {2005}, |
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3321 note = {{MIREX} genre classification contest}, |
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3322 source={OwnPublication}, |
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3323 sourcetype={Other}, |
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3324 } |
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3325 |
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3326 @UNPUBLISHED{mirex2005note, |
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3327 author = {Lacoste, Alexandre and Eck, Douglas}, |
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3328 title = {Onset Detection with Artificial Neural Networks}, |
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3329 year = {2005}, |
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3330 note = {{MIREX} note onset detection contest}, |
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3331 source={OwnPublication}, |
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3332 sourcetype={Other}, |
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3333 } |
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3334 |
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3335 @UNPUBLISHED{mirex2005tempo, |
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3336 author = {Eck, Douglas and Casagrande, Norman}, |
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3337 title = {A Tempo-Extraction Algorithm Using an Autocorrelation Phase Matrix and Shannon Entropy}, |
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3338 year = {2005}, |
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Frederic Bastien <nouiz@nouiz.org>
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3339 note = {{MIREX} tempo extraction contest (www.music-ir.org/\-evaluation/\-mirex-results)}, |
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3340 source={OwnPublication}, |
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3341 sourcetype={Other}, |
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3342 } |
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3343 |
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3344 @INPROCEEDINGS{mitacs-insurance01, |
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3345 author = {Bengio, Yoshua and Chapados, Nicolas and Dugas, Charles and Ghosn, Joumana and Takeuchi, Ichiro and Vincent, Pascal}, |
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3346 title = {High-Dimensional Data Inference for Automobile Insurance Premia Estimation}, |
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3347 booktitle = {Presented at the 2001 MITACS Annual Meeting}, |
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3348 year = {2001}, |
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3349 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/mitacs_insurance.ps}, |
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3350 topics={HighDimensional,Mining},cat={C}, |
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3351 } |
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3352 |
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3353 @INPROCEEDINGS{Morin+al-2005, |
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3354 author = {Morin, Frederic and Bengio, Yoshua}, |
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3355 editor = {Cowell, Robert G. and Ghahramani, Zoubin}, |
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3356 title = {Hierarchical Probabilistic Neural Network Language Model}, |
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3357 booktitle = {Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics}, |
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3358 year = {2005}, |
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3359 pages = {246--252}, |
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3360 publisher = {Society for Artificial Intelligence and Statistics}, |
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3361 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/hierarchical-nnlm-aistats05.pdf}, |
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3362 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.}, |
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3363 topics={Language},cat={C}, |
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3364 } |
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3365 |
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3366 @TECHREPORT{Nadeau-inference-TR99, |
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3367 author = {Nadeau, Claude and Bengio, Yoshua}, |
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3368 title = {Inference and the Generalization Error}, |
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3369 number = {99s-45}, |
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3370 year = {1999}, |
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Frederic Bastien <nouiz@nouiz.org>
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3371 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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3372 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/techrep.pdf}, |
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3373 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 dont tend to be too liberal like other tests currently available, and have good power.}, |
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3374 topics={Comparative},cat={T}, |
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3375 } |
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3376 |
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3377 @INPROCEEDINGS{nadeau:2000:nips, |
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3378 author = {Nadeau, Claude and Bengio, Yoshua}, |
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3379 title = {Inference for the Generalization Error}, |
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3380 year = {2000}, |
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3381 pages = {307--313}, |
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3382 crossref = {NIPS12-shorter}, |
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3383 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).}, |
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3384 topics={Comparative},cat={C}, |
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3385 } |
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3386 |
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3387 @ARTICLE{nadeau:2001, |
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3388 author = {Nadeau, Claude and Bengio, Yoshua}, |
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3389 title = {Inference for the Generalization Error}, |
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|
3390 journal = {Machine Learning}, |
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|
3391 year = {2001}, |
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|
3392 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.}, |
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3393 topics={Comparative},cat={J}, |
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3394 } |
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3395 |
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3396 @ARTICLE{NC06, |
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3397 author = {Bengio, Yoshua and Monperrus, Martin and Larochelle, Hugo}, |
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3398 title = {Nonlocal Estimation of Manifold Structure}, |
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3399 journal = {Neural Computation}, |
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3400 volume = {18}, |
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3401 year = {2006}, |
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3402 pages = {2509--2528}, |
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3403 abstract = {We claim and present arguments to the effect that a large class of manifold |
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|
3404 learning algorithms that are essentially local and can be framed as |
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3405 kernel learning algorithms will suffer from the curse of dimensionality, at |
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3406 the dimension of the true underlying manifold. This observation suggests |
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3407 to explore non-local manifold learning algorithms which attempt to discover |
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3408 shared structure in the tangent planes at different positions. A criterion for |
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3409 such an algorithm is proposed and experiments estimating a tangent plane |
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3410 prediction function are presented, showing its advantages with respect to |
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3411 local manifold learning algorithms: it is able to generalize very far from |
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3412 training data (on learning handwritten character image rotations), where a |
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3413 local non-parametric method fails.}, |
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3414 topics={HighDimensional,Kernel,Unsupervised},cat={J}, |
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3415 } |
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3416 |
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3417 @INPROCEEDINGS{NIPS1-short, |
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3418 editor = {Touretzky, D. S.}, |
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3419 title = {Advances in Neural Information Processing Systems 1 (NIPS'88)}, |
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3420 booktitle = {NIPS 1}, |
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3421 year = {-1}, |
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3422 publisher = {Morgan Kaufmann} |
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3423 } |
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3424 |
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3425 |
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diff
changeset
|
3426 @INPROCEEDINGS{NIPS10-short, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3427 editor = {Jordan, M.I. and Kearns, M.J. and Solla, S.A.}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3428 title = {Advances in Neural Information Processing Systems 10 (NIPS'97)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3429 booktitle = {NIPS 10}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3430 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3431 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3432 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3433 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3434 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3435 @INPROCEEDINGS{NIPS11, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3436 editor = {Kearns, M.J. and Solla, S.A.}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3437 title = {Advances in Neural Information Processing Systems 11 (NIPS'98)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3438 booktitle = {Advances in Neural Information Processing Systems 11 (NIPS'98)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3439 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3440 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3441 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3442 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3443 @INPROCEEDINGS{NIPS11-short, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3444 editor = {Kearns, M.J. and Solla, S.A.}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3445 title = {Advances in Neural Information Processing Systems 11 (NIPS'98)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3446 booktitle = {NIPS 11}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3447 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3448 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3449 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3450 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3451 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3452 @INPROCEEDINGS{NIPS12-short, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3453 editor = {Solla, S.A. and Leen, T. K.}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3454 title = {Advances in Neural Information Processing Systems 12 (NIPS'99)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3455 booktitle = {NIPS 12}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3456 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3457 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3458 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3459 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3460 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3461 @INPROCEEDINGS{NIPS13-short, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3462 editor = {Leen, T. K. and Dietterich, T.G.}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3463 title = {Advances in Neural Information Processing Systems 13 (NIPS'00)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3464 booktitle = {NIPS 13}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3465 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3466 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3467 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3468 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3469 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3470 @INPROCEEDINGS{NIPS14, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3471 editor = {Dietterich, T.G. and Becker, S. and Ghahramani, Zoubin}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3472 title = {Advances in Neural Information Processing Systems 14 (NIPS'01)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3473 booktitle = {Advances in Neural Information Processing Systems 14 (NIPS'01)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3474 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3475 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3476 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3477 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3478 @INPROCEEDINGS{NIPS14-short, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3479 editor = {Dietterich, T.G. and Becker, S. and Ghahramani, Zoubin}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3480 title = {Advances in Neural Information Processing Systems 14 (NIPS'01)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3481 booktitle = {NIPS 14}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3482 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3483 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3484 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3485 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3486 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3487 @INPROCEEDINGS{NIPS15-short, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3488 editor = {Becker, S. and Thrun, Sebastian}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3489 title = {Advances in Neural Information Processing Systems 15 (NIPS'02)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3490 booktitle = {NIPS 15}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3491 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3492 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3493 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3494 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3495 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3496 @INPROCEEDINGS{NIPS16-short, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3497 editor = {Becker, S. and Saul, L. and {Sch{\"{o}}lkopf}, Bernhard}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3498 title = {Advances in Neural Information Processing Systems 16 (NIPS'03)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3499 booktitle = {NIPS 16}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3500 year = {-1} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3501 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3502 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3503 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3504 @INPROCEEDINGS{NIPS17-short, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3505 editor = {Saul, Lawrence K. and Weiss, Yair and Bottou, {L{\'{e}}on}}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3506 title = {Advances in Neural Information Processing Systems 17 (NIPS'04)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3507 booktitle = {NIPS 17}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3508 year = {-1} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3509 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3510 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3511 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3512 @INPROCEEDINGS{NIPS18-short, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3513 editor = {Weiss, Yair and {Sch{\"{o}}lkopf}, Bernhard and Platt, John}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3514 title = {Advances in Neural Information Processing Systems 18 (NIPS'05)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3515 booktitle = {NIPS 18}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3516 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3517 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3518 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3519 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3520 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3521 @INPROCEEDINGS{NIPS19-short, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3522 editor = {{Sch{\"{o}}lkopf}, Bernhard and Platt, John and Hoffman, Thomas}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3523 title = {Advances in Neural Information Processing Systems 19 (NIPS'06)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3524 booktitle = {NIPS 19}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3525 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3526 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3527 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3528 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3529 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3530 @INPROCEEDINGS{NIPS2-short, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3531 editor = {Touretzky, D. S.}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3532 title = {Advances in Neural Information Processing Systems 2 (NIPS'89)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3533 booktitle = {NIPS 2}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3534 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3535 publisher = {Morgan Kaufmann} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3536 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3537 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3538 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3539 @INPROCEEDINGS{NIPS20-short, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3540 editor = {Platt, John and Koller, D. and Singer, Yoram and Roweis, S.}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3541 title = {Advances in Neural Information Processing Systems 20 (NIPS'07)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3542 booktitle = {NIPS 20}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3543 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3544 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3545 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3546 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
3547 |
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3548 @INPROCEEDINGS{NIPS2003_AA65, |
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3549 author = {Bengio, Yoshua and Grandvalet, Yves}, |
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3550 keywords = {cross validation, error bars, generalization error inference, k-fold cross-validation, model selection, statistical comparison of algorithms, variance estimate}, |
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3551 title = {No Unbiased Estimator of the Variance of K-Fold Cross-Validation}, |
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3552 year = {2004}, |
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3553 publisher = {MIT Press}, |
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3554 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/var-kfold-part1-nips.pdf}, |
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3555 crossref = {NIPS16}, |
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3556 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.}, |
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3557 topics={Comparative},cat={C}, |
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3558 } |
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3559 |
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3560 @INCOLLECTION{NIPS2005_424, |
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3561 author = {Bengio, Yoshua and Delalleau, Olivier and Le Roux, Nicolas}, |
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3562 title = {The Curse of Highly Variable Functions for Local Kernel Machines}, |
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3563 year = {2006}, |
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3564 pages = {107--114}, |
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3565 crossref = {NIPS18-shorter}, |
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3566 abstract = {We present a series of theoretical arguments supporting the claim that a |
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3567 large class of modern learning algorithms that rely solely on the smoothness |
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3568 prior with similarity between examples expressed with a local |
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3569 kernel are sensitive to the curse of dimensionality, or more precisely |
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3570 to the variability of the target. Our discussion covers supervised, semisupervised |
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3571 and unsupervised learning algorithms. These algorithms are |
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3572 found to be local in the sense that crucial properties of the learned function |
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3573 at x depend mostly on the neighbors of x in the training set. This |
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3574 makes them sensitive to the curse of dimensionality, well studied for |
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3575 classical non-parametric statistical learning. We show in the case of the |
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3576 Gaussian kernel that when the function to be learned has many variations, |
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3577 these algorithms require a number of training examples proportional to |
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3578 the number of variations, which could be large even though there may exist |
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3579 short descriptions of the target function, i.e. their Kolmogorov complexity |
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3580 may be low. This suggests that there exist non-local learning |
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3581 algorithms that at least have the potential to learn about such structured |
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3582 but apparently complex functions (because locally they have many variations), |
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3583 while not using very specific prior domain knowledge.}, |
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3584 topics={HighDimensional,Kernel,Unsupervised},cat={C}, |
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3585 } |
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3586 |
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3587 @INPROCEEDINGS{NIPS2005_456, |
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3588 author = {K{\'{e}}gl, Bal{\'{a}}zs and Wang, Ligen}, |
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3589 title = {Boosting on Manifolds: Adaptive Regularization of Base Classifiers}, |
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3590 year = {2005}, |
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3591 pages = {665--672}, |
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3592 crossref = {NIPS17-shorter}, |
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3593 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 ADABOOSTs 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.}, |
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3594 topics={Boosting},cat={C}, |
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3595 } |
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3596 |
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3597 @INCOLLECTION{NIPS2005_519, |
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3598 author = {Grandvalet, Yves and Bengio, Yoshua}, |
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3599 title = {Semi-supervised Learning by Entropy Minimization}, |
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3600 year = {2005}, |
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3601 pages = {529--236}, |
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3602 crossref = {NIPS17-shorter}, |
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3603 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.}, |
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3604 topics={Unsupervised},cat={C}, |
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3605 } |
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3606 |
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3607 @INPROCEEDINGS{NIPS2005_539, |
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3608 author = {Bengio, Yoshua and Larochelle, Hugo and Vincent, Pascal}, |
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3609 title = {Non-Local Manifold Parzen Windows}, |
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3610 year = {2006}, |
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3611 crossref = {NIPS18-shorter}, |
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3612 abstract = {To escape from the curse of dimensionality, we claim that one can learn |
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3613 non-local functions, in the sense that the value and shape of the learned |
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3614 function at x must be inferred using examples that may be far from x. |
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3615 With this objective, we present a non-local non-parametric density estimator. |
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3616 It builds upon previously proposed Gaussian mixture models with |
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3617 regularized covariance matrices to take into account the local shape of |
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3618 the manifold. It also builds upon recent work on non-local estimators of |
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3619 the tangent plane of a manifold, which are able to generalize in places |
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3620 with little training data, unlike traditional, local, non-parametric models.}, |
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3621 topics={HighDimensional,Kernel,Unsupervised},cat={C}, |
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3622 } |
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3623 |
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3624 @INPROCEEDINGS{NIPS2005_583, |
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3625 author = {Bengio, Yoshua and Le Roux, Nicolas and Vincent, Pascal and Delalleau, Olivier and Marcotte, Patrice}, |
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3626 title = {Convex Neural Networks}, |
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3627 year = {2006}, |
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3628 pages = {123--130}, |
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3629 crossref = {NIPS18-shorter}, |
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3630 abstract = {Convexity has recently received a lot of attention in the machine learning |
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3631 community, and the lack of convexity has been seen as a major disadvantage |
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3632 of many learning algorithms, such as multi-layer artificial neural |
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3633 networks. We show that training multi-layer neural networks in which the |
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3634 number of hidden units is learned can be viewed as a convex optimization |
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3635 problem. This problem involves an infinite number of variables, but can be |
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3636 solved by incrementally inserting a hidden unit at a time, each time finding |
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3637 a linear classifier that minimizes a weighted sum of errors.}, |
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3638 topics={Boosting},cat={C}, |
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3639 } |
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3640 |
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3641 @INPROCEEDINGS{NIPS2005_663, |
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3642 author = {Rivest, Fran{\c c}ois and Bengio, Yoshua and Kalaska, John}, |
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3643 title = {Brain Inspired Reinforcement Learning}, |
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3644 year = {2005}, |
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3645 pages = {1129--1136}, |
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3646 crossref = {NIPS17-shorter}, |
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3647 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.}, |
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3648 topics={BioRules},cat={C}, |
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3649 } |
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3650 |
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3651 @INCOLLECTION{NIPS2005_691, |
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3652 author = {Bengio, Yoshua and Monperrus, Martin}, |
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3653 title = {Non-Local Manifold Tangent Learning}, |
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3654 year = {2005}, |
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3655 pages = {129--136}, |
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3656 crossref = {NIPS17-shorter}, |
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3657 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.}, |
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3658 topics={HighDimensional,Unsupervised},cat={C}, |
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3659 } |
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3660 |
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3661 @INPROCEEDINGS{NIPS2005_874, |
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Frederic Bastien <nouiz@nouiz.org>
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3662 author = {K{\'{e}}gl, Bal{\'{a}}zs}, |
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3663 title = {Generalization Error and Algorithmic Convergence of Median Boosting}, |
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Frederic Bastien <nouiz@nouiz.org>
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3664 year = {2005}, |
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Frederic Bastien <nouiz@nouiz.org>
parents:
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3665 pages = {657--664}, |
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Frederic Bastien <nouiz@nouiz.org>
parents:
diff
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|
3666 crossref = {NIPS17-shorter}, |
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Frederic Bastien <nouiz@nouiz.org>
parents:
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|
3667 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.}, |
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Frederic Bastien <nouiz@nouiz.org>
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3668 topics={Boosting},cat={C}, |
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|
3669 } |
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Frederic Bastien <nouiz@nouiz.org>
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3670 |
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Frederic Bastien <nouiz@nouiz.org>
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|
3671 @INPROCEEDINGS{NIPS2007-56, |
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Frederic Bastien <nouiz@nouiz.org>
parents:
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|
3672 author = {Le Roux, Nicolas and Manzagol, Pierre-Antoine and Bengio, Yoshua}, |
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Frederic Bastien <nouiz@nouiz.org>
parents:
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|
3673 title = {Topmoumoute online natural gradient algorithm}, |
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Frederic Bastien <nouiz@nouiz.org>
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|
3674 year = {2008}, |
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Frederic Bastien <nouiz@nouiz.org>
parents:
diff
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|
3675 crossref = {NIPS20-shorter}, |
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Frederic Bastien <nouiz@nouiz.org>
parents:
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|
3676 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.} |
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3677 } |
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Frederic Bastien <nouiz@nouiz.org>
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3678 |
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Frederic Bastien <nouiz@nouiz.org>
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3679 @INPROCEEDINGS{NIPS2007-812, |
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Frederic Bastien <nouiz@nouiz.org>
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3680 author = {Chapados, Nicolas and Bengio, Yoshua}, |
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Frederic Bastien <nouiz@nouiz.org>
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3681 title = {Augmented Functional Time Series Representation and Forecasting with Gaussian Processes}, |
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Frederic Bastien <nouiz@nouiz.org>
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3682 year = {2008}, |
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Frederic Bastien <nouiz@nouiz.org>
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|
3683 pages = {265--272}, |
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Frederic Bastien <nouiz@nouiz.org>
parents:
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|
3684 crossref = {NIPS20-shorter}, |
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Frederic Bastien <nouiz@nouiz.org>
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|
3685 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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3686 } |
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Frederic Bastien <nouiz@nouiz.org>
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3687 |
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Frederic Bastien <nouiz@nouiz.org>
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3688 @INPROCEEDINGS{NIPS2007-925, |
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Frederic Bastien <nouiz@nouiz.org>
parents:
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3689 author = {Le Roux, Nicolas and Bengio, Yoshua and Lamblin, Pascal and Joliveau, Marc and K{\'{e}}gl, Bal{\'{a}}zs}, |
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Frederic Bastien <nouiz@nouiz.org>
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3690 title = {Learning the 2-D Topology of Images}, |
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Frederic Bastien <nouiz@nouiz.org>
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3691 year = {2008}, |
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Frederic Bastien <nouiz@nouiz.org>
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|
3692 pages = {841--848}, |
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Frederic Bastien <nouiz@nouiz.org>
parents:
diff
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|
3693 crossref = {NIPS20-shorter}, |
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Frederic Bastien <nouiz@nouiz.org>
parents:
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|
3694 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.} |
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3695 } |
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Frederic Bastien <nouiz@nouiz.org>
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|
3696 |
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Frederic Bastien <nouiz@nouiz.org>
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|
3697 @INPROCEEDINGS{NIPS21, |
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Frederic Bastien <nouiz@nouiz.org>
parents:
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|
3698 editor = {Koller, D. and Schuurmans, Dale and Bengio, Yoshua and Bottou, {L{\'{e}}on}}, |
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Frederic Bastien <nouiz@nouiz.org>
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|
3699 title = {Advances in Neural Information Processing Systems 21 (NIPS'08)}, |
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Frederic Bastien <nouiz@nouiz.org>
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|
3700 booktitle = {Advances in Neural Information Processing Systems 21 (NIPS'08)}, |
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Frederic Bastien <nouiz@nouiz.org>
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|
3701 year = {-1}, |
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Frederic Bastien <nouiz@nouiz.org>
parents:
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|
3702 publisher = {Nips Foundation (http://books.nips.cc)} |
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Frederic Bastien <nouiz@nouiz.org>
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|
3703 } |
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Frederic Bastien <nouiz@nouiz.org>
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|
3704 |
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Frederic Bastien <nouiz@nouiz.org>
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|
3705 @INPROCEEDINGS{NIPS21-short, |
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Frederic Bastien <nouiz@nouiz.org>
parents:
diff
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|
3706 editor = {Koller, D. and Schuurmans, Dale and Bengio, Yoshua and Bottou, {L{\'{e}}on}}, |
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Frederic Bastien <nouiz@nouiz.org>
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|
3707 title = {Advances in Neural Information Processing Systems 21 (NIPS'08)}, |
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Frederic Bastien <nouiz@nouiz.org>
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|
3708 booktitle = {NIPS 21}, |
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Frederic Bastien <nouiz@nouiz.org>
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diff
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|
3709 year = {-1}, |
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Frederic Bastien <nouiz@nouiz.org>
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|
3710 publisher = {Nips Foundation (http://books.nips.cc)} |
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Frederic Bastien <nouiz@nouiz.org>
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|
3711 } |
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Frederic Bastien <nouiz@nouiz.org>
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|
3712 |
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Frederic Bastien <nouiz@nouiz.org>
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diff
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|
3713 |
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Frederic Bastien <nouiz@nouiz.org>
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|
3714 @INPROCEEDINGS{NIPS22-short, |
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Frederic Bastien <nouiz@nouiz.org>
parents:
diff
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|
3715 editor = {Bengio, Yoshua and Schuurmans, Dale and Williams, Christopher and Lafferty, John and Culotta, Aron}, |
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Frederic Bastien <nouiz@nouiz.org>
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|
3716 title = {Advances in Neural Information Processing Systems 22 (NIPS'09)}, |
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Frederic Bastien <nouiz@nouiz.org>
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|
3717 booktitle = {NIPS 22}, |
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Frederic Bastien <nouiz@nouiz.org>
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|
3718 year = {-1} |
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Frederic Bastien <nouiz@nouiz.org>
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|
3719 } |
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Frederic Bastien <nouiz@nouiz.org>
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diff
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|
3720 |
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Frederic Bastien <nouiz@nouiz.org>
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|
3721 |
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Frederic Bastien <nouiz@nouiz.org>
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|
3722 @INPROCEEDINGS{NIPS3, |
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Frederic Bastien <nouiz@nouiz.org>
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|
3723 editor = {Lipmann, R. P. and Moody, J. E. and Touretzky, D. S.}, |
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Frederic Bastien <nouiz@nouiz.org>
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|
3724 title = {Advances in Neural Information Processing Systems 3 (NIPS'90)}, |
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Frederic Bastien <nouiz@nouiz.org>
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|
3725 booktitle = {Advances in Neural Information Processing Systems 3 (NIPS'90)}, |
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Frederic Bastien <nouiz@nouiz.org>
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|
3726 year = {-1}, |
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Frederic Bastien <nouiz@nouiz.org>
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|
3727 publisher = {Morgan Kaufmann} |
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Frederic Bastien <nouiz@nouiz.org>
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|
3728 } |
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Frederic Bastien <nouiz@nouiz.org>
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|
3729 |
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Frederic Bastien <nouiz@nouiz.org>
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|
3730 @INPROCEEDINGS{NIPS3-short, |
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Frederic Bastien <nouiz@nouiz.org>
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|
3731 editor = {Lipmann, R. P. and Moody, J. E. and Touretzky, D. S.}, |
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Frederic Bastien <nouiz@nouiz.org>
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|
3732 title = {Advances in Neural Information Processing Systems 3 (NIPS'90)}, |
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Frederic Bastien <nouiz@nouiz.org>
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|
3733 booktitle = {NIPS 3}, |
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Frederic Bastien <nouiz@nouiz.org>
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|
3734 year = {-1}, |
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Frederic Bastien <nouiz@nouiz.org>
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|
3735 publisher = {Morgan Kaufmann} |
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|
3736 } |
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Frederic Bastien <nouiz@nouiz.org>
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|
3737 |
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Frederic Bastien <nouiz@nouiz.org>
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3738 |
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Frederic Bastien <nouiz@nouiz.org>
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|
3739 @INPROCEEDINGS{NIPS4-short, |
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Frederic Bastien <nouiz@nouiz.org>
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|
3740 editor = {Moody, J. E. and Hanson, S. J. and Lipmann, R. P.}, |
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Frederic Bastien <nouiz@nouiz.org>
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|
3741 title = {Advances in Neural Information Processing Systems 4 (NIPS'91)}, |
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Frederic Bastien <nouiz@nouiz.org>
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3742 booktitle = {NIPS 4}, |
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|
3743 year = {-1}, |
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Frederic Bastien <nouiz@nouiz.org>
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3744 publisher = {Morgan Kaufmann} |
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3745 } |
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Frederic Bastien <nouiz@nouiz.org>
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3746 |
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Frederic Bastien <nouiz@nouiz.org>
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3747 |
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Frederic Bastien <nouiz@nouiz.org>
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|
3748 @INPROCEEDINGS{NIPS5, |
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Frederic Bastien <nouiz@nouiz.org>
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|
3749 editor = {Giles, C.L. and Hanson, S. J. and Cowan, J. D.}, |
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|
3750 title = {Advances in Neural Information Processing Systems 5 (NIPS'92)}, |
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Frederic Bastien <nouiz@nouiz.org>
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|
3751 booktitle = {Advances in Neural Information Processing Systems 5 (NIPS'92)}, |
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Frederic Bastien <nouiz@nouiz.org>
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|
3752 year = {-1}, |
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Frederic Bastien <nouiz@nouiz.org>
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3753 publisher = {Morgan Kaufmann} |
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3754 } |
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3755 |
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|
3756 @INPROCEEDINGS{NIPS5-short, |
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Frederic Bastien <nouiz@nouiz.org>
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|
3757 editor = {Giles, C.L. and Hanson, S. J. and Cowan, J. D.}, |
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|
3758 title = {Advances in Neural Information Processing Systems 5 (NIPS'92)}, |
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|
3759 booktitle = {NIPS 5}, |
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|
3760 year = {-1}, |
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|
3761 publisher = {Morgan Kaufmann} |
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|
3762 } |
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Frederic Bastien <nouiz@nouiz.org>
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|
3763 |
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Frederic Bastien <nouiz@nouiz.org>
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|
3764 |
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|
3765 @INPROCEEDINGS{NIPS6-short, |
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|
3766 editor = {Cowan, J. D. and Tesauro, G. and Alspector, J.}, |
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3767 title = {Advances in Neural Information Processing Systems 6 (NIPS'93)}, |
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3768 booktitle = {NIPS 6}, |
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3769 year = {-1}, |
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3770 publisher = {MIT Press} |
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3771 } |
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3772 |
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3773 |
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3774 @INPROCEEDINGS{NIPS7-short, |
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3775 editor = {Tesauro, G. and Touretzky, D. S. and Leen, T. K.}, |
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3776 title = {Advances in Neural Information Processing Systems 7 (NIPS'94)}, |
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3777 booktitle = {NIPS 7}, |
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3778 year = {-1}, |
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3779 publisher = {MIT Press} |
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3780 } |
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3781 |
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3782 |
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3783 @INPROCEEDINGS{NIPS8-short, |
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3784 editor = {Touretzky, D. S. and Mozer, M. and Hasselmo, M.E.}, |
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3785 title = {Advances in Neural Information Processing Systems 8 (NIPS'95)}, |
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3786 booktitle = {NIPS 8}, |
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3787 year = {-1}, |
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3788 publisher = {MIT Press} |
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3789 } |
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3790 |
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3791 |
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3792 @INPROCEEDINGS{NIPS9-short, |
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Frederic Bastien <nouiz@nouiz.org>
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3793 editor = {Mozer, M. and Jordan, M.I. and Petsche, T.}, |
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3794 title = {Advances in Neural Information Processing Systems 9 (NIPS'96)}, |
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3795 booktitle = {NIPS 9}, |
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3796 year = {-1}, |
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3797 publisher = {MIT Press} |
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3798 } |
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3799 |
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3800 |
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3801 @INPROCEEDINGS{nnlm:2001:nips, |
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3802 author = {Bengio, Yoshua and Ducharme, R{\'{e}}jean and Vincent, Pascal}, |
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3803 title = {A Neural Probabilistic Language Model}, |
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3804 year = {2001}, |
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3805 crossref = {NIPS13-shorter}, |
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3806 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 |
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3807 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.}, |
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3808 topics={Markov,Unsupervised,Language},cat={C}, |
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3809 } |
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3810 |
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3811 @INPROCEEDINGS{nsvn:2000:ijcnn, |
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3812 author = {Vincent, Pascal and Bengio, Yoshua}, |
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3813 title = {A Neural Support Vector Network Architecture with Adaptive Kernels}, |
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3814 booktitle = {International Joint Conference on Neural Networks 2000}, |
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3815 volume = {V}, |
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3816 year = {2000}, |
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3817 pages = {187--192}, |
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3818 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/nsvn.pdf}, |
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3819 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.}, |
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3820 topics={Kernel},cat={C}, |
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3821 } |
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3822 |
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3823 @INPROCEEDINGS{Ouimet+al-2005, |
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3824 author = {Ouimet, Marie and Bengio, Yoshua}, |
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3825 editor = {Cowell, Robert G. and Ghahramani, Zoubin}, |
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3826 title = {Greedy Spectral Embedding}, |
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3827 booktitle = {Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics}, |
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3828 year = {2005}, |
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3829 pages = {253--260}, |
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3830 publisher = {Society for Artificial Intelligence and Statistics}, |
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3831 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/greedy-kernel-aistats05.pdf}, |
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3832 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.}, |
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3833 topics={HighDimensional,kenel},cat={C}, |
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3834 } |
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3835 |
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3836 @MASTERSTHESIS{Ouimet-Msc-2004, |
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3837 author = {Ouimet, Marie}, |
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3838 keywords = {algorithmes voraces., apprentissage non-supervis{\'{e}}, m{\'{e}}thodes spectrales, noyaux, r{\'{e}}duction de dimensionnalit{\'{e}}}, |
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3839 title = {R{\'{e}}duction de dimensionnalit{\'{e}} non lin{\'{e}}aire et vorace}, |
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3840 year = {2004}, |
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3841 school = {Universit{\'{e}} de Montr{\'{e}}al}, |
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3842 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.} |
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3843 } |
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3844 |
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3845 @ARTICLE{paiement+bengio+eck:aij, |
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3846 author = {Paiement, Jean-Fran{\c c}ois and Bengio, Samy and Eck, Douglas}, |
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3847 title = {Probabilistic Models for Melodic Prediction}, |
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3848 journal = {Artificial Intelligence Journal}, |
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3849 volume = {173}, |
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3850 year = {2009}, |
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3851 pages = {1266-1274}, |
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3852 source={OwnPublication}, |
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3853 sourcetype={Journal}, |
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3854 } |
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3855 |
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3856 @INPROCEEDINGS{paiement+eck+bengio+barber:icml2005, |
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3857 author = {Paiement, Jean-Fran{\c c}ois and Eck, Douglas and Bengio, Samy and Barber, D.}, |
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3858 title = {A graphical model for chord progressions embedded in a psychoacoustic space}, |
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3859 year = {2005}, |
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3860 pages = {641--648}, |
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3861 publisher = {ACM Press}, |
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3862 crossref = {ICML05}, |
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3863 source={OwnPublication}, |
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3864 sourcetype={Conference}, |
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3865 } |
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3866 |
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3867 @INPROCEEDINGS{paiement+eck+bengio:ccai2006, |
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3868 author = {Paiement, Jean-Fran{\c c}ois and Eck, Douglas and Bengio, Samy}, |
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3869 editor = {Lamontagne, Luc and Marchand, Mario}, |
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3870 title = {Probabilistic Melodic Harmonization}, |
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3871 booktitle = {Canadian Conference on AI}, |
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3872 series = {Lecture Notes in Computer Science}, |
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3873 volume = {4013}, |
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3874 year = {2006}, |
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3875 pages = {218-229}, |
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3876 publisher = {Springer}, |
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3877 source={OwnPublication}, |
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3878 sourcetype={Conference}, |
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3879 } |
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3880 |
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3881 @INPROCEEDINGS{paiement+eck+bengio:ismir2005, |
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3882 author = {Paiement, Jean-Fran{\c c}ois and Eck, Douglas and Bengio, Samy}, |
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3883 title = {A Probabilistic Model for Chord Progressions}, |
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3884 booktitle = {{Proceedings of the 6th International Conference on Music Information Retrieval ({ISMIR} 2005)}}, |
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3885 year = {2005}, |
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3886 pages = {312-319}, |
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3887 source={OwnPublication}, |
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3888 sourcetype={Conference}, |
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3889 } |
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3890 |
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3891 @INPROCEEDINGS{paiement+grandvalet+bengio+eck:icml2008, |
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3892 author = {Paiement, Jean-Fran{\c c}ois and Grandvalet, Yves and Bengio, Samy and Eck, Douglas}, |
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3893 title = {A generative model for rhythms}, |
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3894 year = {2008}, |
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3895 pages = {}, |
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3896 crossref = {ICML06-shorter}, |
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3897 source={OwnPublication}, |
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3898 sourcetype={Conference}, |
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3899 } |
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3900 |
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3901 @UNPUBLISHED{paiement+grandvalet+bengio+eck:nipsworkshop2007, |
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3902 author = {Paiement, Jean-Fran{\c c}ois and Grandvalet, Yves and Bengio, Samy and Eck, Douglas}, |
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3903 title = {A generative model for rhythms}, |
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3904 year = {2007}, |
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3905 note = {NIPS 2007 Workshop on Music, Brain and Cognition}, |
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3906 source={OwnPublication}, |
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3907 sourcetype={Workshop}, |
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3908 optkey={""}, |
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3909 optmonth={""}, |
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3910 optannote={""}, |
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3911 } |
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3912 |
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3913 @MASTERSTHESIS{Paiement-Msc-2003, |
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3914 author = {Paiement, Jean-Fran{\c c}ois}, |
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3915 keywords = {algorithmes, apprentissage, apprentissage non supervis{\'{e}}, forage de donn{\'{e}}es, noyaux, r{\'{e}}duction de dimensions, statistique, Statistiques}, |
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3916 title = {G{\'{e}}n{\'{e}}ralisation d'algorithmes de r{\'{e}}duction de dimension}, |
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3917 year = {2003}, |
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3918 school = {Universit{\'{e}} de Montr{\'{e}}al}, |
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3919 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. |
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3920 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. |
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3921 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. |
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3922 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. |
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3923 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.} |
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3924 } |
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3925 |
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3926 @ARTICLE{perez+gers+schmidhuber+eck:2002, |
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3927 author = {Perez-Ortiz, J. A. and Gers, F. A. and Eck, Douglas and Schmidhuber, Juergen}, |
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3928 title = {{K}alman filters improve {LSTM} network performance in problems unsolvable by traditional recurrent nets}, |
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3929 journal = {Neural Networks}, |
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3930 volume = {16}, |
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3931 number = {2}, |
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3932 year = {2003}, |
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3933 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.}, |
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3934 source={OwnPublication}, |
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3935 sourcetype={Journal}, |
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3936 } |
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3937 |
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3938 @ARTICLE{perez+gers+schmidhuber+eck:2003, |
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3939 author = {Perez-Ortiz, J. A. and Gers, F. A. and Eck, Douglas and Schmidhuber, Juergen}, |
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3940 title = {{K}alman filters improve {LSTM} network performance in problems unsolvable by traditional recurrent nets}, |
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3941 journal = {Neural Networks}, |
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3942 volume = {16}, |
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3943 number = {2}, |
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3944 year = {2003}, |
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|
3945 pages = {241--250}, |
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|
3946 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.}, |
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3947 source={OwnPublication}, |
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3948 sourcetype={Journal}, |
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3949 } |
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3950 |
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3951 @INPROCEEDINGS{perez+schmidhuber+gers+eck:icannB2002, |
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3952 author = {Perez-Ortiz, J. A. and Schmidhuber, Juergen and Gers, F. A. and Eck, Douglas}, |
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3953 editor = {Dorronsoro, J.}, |
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3954 title = {Improving Long-Term Online Prediction with {Decoupled Extended Kalman Filters}}, |
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3955 booktitle = {{Artificial Neural Networks -- ICANN 2002 (Proceedings)}}, |
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3956 year = {2002}, |
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3957 pages = {1055--1060}, |
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3958 publisher = {Springer}, |
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3959 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.}, |
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3960 source={OwnPublication}, |
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3961 sourcetype={Conference}, |
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3962 } |
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3963 |
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3964 @TECHREPORT{Pigeon-Bengio-96-aH-TR, |
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3965 author = {Pigeon, Steven and Bengio, Yoshua}, |
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3966 title = {A Memory-Efficient Huffman Adaptive Coding Algorithm for Very Large Sets of Symbols}, |
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3967 number = {\#1081}, |
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3968 year = {1997}, |
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3969 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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3970 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/HuffAdapt.pdf}, |
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3971 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.}, |
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3972 topics={Compression},cat={T}, |
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3973 } |
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3974 |
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3975 @INPROCEEDINGS{Pigeon-dcc98, |
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3976 author = {Pigeon, Steven and Bengio, Yoshua}, |
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3977 editor = {Society, {IEEE} Computer}, |
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3978 title = {A Memory-Efficient Adaptive Huffman Coding Algorithm for Very Large Sets of Symbols}, |
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3979 booktitle = {Data Compression Conference}, |
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3980 year = {1998}, |
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3981 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/dcc98.pdf}, |
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3982 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 |
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3983 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.}, |
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3984 topics={Compression},cat={C}, |
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3985 } |
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3986 |
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3987 @INPROCEEDINGS{Pigeon-dcc99, |
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3988 author = {Pigeon, Steven and Bengio, Yoshua}, |
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3989 editor = {Society, {IEEE} Computer}, |
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3990 title = {Binary Pseudowavelets and Applications to Bilevel Image Processing}, |
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3991 booktitle = {Data Compression Conference}, |
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3992 year = {1999}, |
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3993 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/dcc99.pdf}, |
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3994 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.}, |
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3995 topics={Compression},cat={C}, |
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3996 } |
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3997 |
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3998 @TECHREPORT{Pigeon-Huffman-TR98, |
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3999 author = {Pigeon, Steven and Bengio, Yoshua}, |
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4000 title = {A Memory-Efficient Adaptive Huffman Coding for Very Large Sets of Symbols revisited}, |
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4001 number = {1095}, |
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4002 year = {1998}, |
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4003 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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4004 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/TechRep_AdaptativeHuffman2.pdf}, |
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4005 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+.}, |
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4006 topics={Compression},cat={T}, |
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4007 } |
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4008 |
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4009 @PHDTHESIS{Pigeon-Phd-2001, |
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4010 author = {Pigeon, Steven}, |
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4011 keywords = {algorithmes, codes adaptatifs, codes de Golomb, codes universels, Compression de donn{\'{e}}es, compression LZ78, LZW, ondelettes, pseudo-ondelettes}, |
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4012 title = {Contributions {\`{a}} la compression de donn{\'{e}}es}, |
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4013 year = {2001}, |
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4014 school = {Universit{\'{e}} de Montr{\'{e}}al}, |
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4015 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}}.} |
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4016 } |
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4017 |
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4018 @ARTICLE{Pigeon98, |
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4019 author = {Pigeon, Steven and Bengio, Yoshua}, |
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4020 title = {Memory-Efficient Adaptive Huffman Coding}, |
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4021 journal = {Dr. Dobb's Journal}, |
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4022 volume = {290}, |
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4023 year = {1998}, |
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4024 pages = {131--135}, |
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4025 topics={Compression},cat={J}, |
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4026 } |
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4027 |
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4028 @INPROCEEDINGS{probnn:2000:ijcnn, |
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4029 author = {Bengio, Yoshua}, |
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4030 title = {Probabilistic Neural Network Models for Sequential Data}, |
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4031 booktitle = {International Joint Conference on Neural Networks 2000}, |
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4032 volume = {V}, |
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4033 year = {2000}, |
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4034 pages = {79--84}, |
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4035 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/81_01.PDF}, |
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4036 abstract = {It has already been shown how Artificial Neural Networks ({ANN}s) can be incorporated into probabilistic models. |
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4037 In this paper we review some of the approaches which have been proposed to incorporate them into probabilistic |
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4038 models of sequential data, such as Hidden {Markov} Models ({HMM}s). We also discuss new developments and new |
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4039 ideas in this area, in particular how {ANN}s can be used to model high-dimensional discrete and continuous data to |
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4040 deal with the curse of dimensionality, and how the ideas proposed in these models could be applied to statistical |
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4041 language modeling to represent longer-term context than allowed by trigram models, while keeping word-order |
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4042 information.}, |
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4043 topics={Markov},cat={C}, |
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4044 } |
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4045 |
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4046 @UNPUBLISHED{pugin+burgoyne+eck+fujinaga:nipsworkshop2007, |
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4047 author = {Pugin, L. and Burgoyne, J. A. and Eck, Douglas and Fujinaga, I.}, |
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4048 title = {Book-adaptive and book-dependant models to accelerate digitalization of early music}, |
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4049 year = {2007}, |
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4050 note = {NIPS 2007 Workshop on Music, Brain and Cognition}, |
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4051 source={OwnPublication}, |
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4052 sourcetype={Workshop}, |
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4053 optkey={""}, |
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4054 optmonth={""}, |
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4055 optannote={""}, |
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4056 } |
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4057 |
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4058 @INPROCEEDINGS{Rahim-97, |
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4059 author = {Rahim, Mazin and Bengio, Yoshua and {LeCun}, Yann}, |
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4060 title = {Discriminative feature and model design for automatic speech recognition}, |
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4061 booktitle = {Proceedings of Eurospeech 1997}, |
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4062 year = {1997}, |
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4063 pages = {75--78}, |
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4064 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/rahim-bengio-lecun-97.ps.gz}, |
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4065 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.}, |
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4066 topics={Speech},cat={C}, |
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4067 } |
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4068 |
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4069 @ARTICLE{Rivest-2009, |
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4070 author = {Rivest, Fran{\c c}ois and Kalaska, John and Bengio, Yoshua}, |
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4071 title = {Alternative Time Representations in Dopamine Models}, |
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4072 journal = {Journal of Computational Neuroscience}, |
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4073 volume = {28}, |
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4074 number = {1}, |
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4075 year = {2009}, |
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4076 pages = {107--130}, |
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4077 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.} |
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4078 } |
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4079 |
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4080 @ARTICLE{schmidhuber+gers+eck:2002, |
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4081 author = {Schmidhuber, Juergen and Gers, F. A. and Eck, Douglas}, |
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4082 title = {Learning Nonregular Languages: A Comparison of Simple Recurrent Networks and {LSTM}}, |
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4083 journal = {Neural Computation}, |
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4084 volume = {14}, |
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4085 number = {9}, |
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4086 year = {2002}, |
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4087 pages = {2039--2041}, |
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4088 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.}, |
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4089 source={OwnPublication}, |
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4090 sourcetype={Journal}, |
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4091 } |
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4092 |
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4093 @TECHREPORT{Schwenk-Bengio-97-TR, |
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4094 author = {Schwenk, Holger and Bengio, Yoshua}, |
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4095 title = {Adaptive Boosting of Neural Networks for Character Recognition}, |
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4096 number = {\#1072}, |
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4097 year = {1997}, |
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4098 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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4099 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/AdaBoostTR.pdf}, |
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4100 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.}, |
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4101 topics={Boosting,Speech},cat={T}, |
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4102 } |
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4103 |
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4104 @INPROCEEDINGS{Schwenk-nips10, |
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4105 author = {Schwenk, Holger and Bengio, Yoshua}, |
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4106 title = {Training Methods for Adaptive Boosting of Neural Networks for Character Recognition}, |
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4107 year = {1998}, |
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4108 crossref = {NIPS10-shorter}, |
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4109 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.}, |
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4110 topics={Boosting,Speech},cat={C}, |
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4111 } |
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4112 |
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4113 @ARTICLE{Schwenk2000, |
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4114 author = {Schwenk, Holger and Bengio, Yoshua}, |
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4115 title = {Boosting Neural Networks}, |
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4116 journal = {Neural Computation}, |
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4117 volume = {12}, |
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4118 number = {8}, |
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4119 year = {2000}, |
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4120 pages = {1869--1887}, |
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4121 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.}, |
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4122 topics={Boosting},cat={J}, |
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4123 } |
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4124 |
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4125 @INPROCEEDINGS{secondorder:2001:nips, |
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4126 author = {Dugas, Charles and Bengio, Yoshua and Belisle, Francois and Nadeau, Claude and Garcia, Rene}, |
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4127 title = {Incorporating Second-Order Functional Knowledge for Better Option Pricing}, |
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4128 year = {2001}, |
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4129 crossref = {NIPS13-shorter}, |
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4130 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.}, |
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4131 topics={Finance},cat={C}, |
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4132 } |
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4133 |
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4134 @ARTICLE{Sonnenburg+al-2007, |
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4135 author = {Sonnenburg, Soeren and et al. and Vincent, Pascal}, |
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4136 title = {The Need for Open Source Software in Machine Learning.}, |
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4137 year = {2007}, |
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4138 note = {institution: Fraunhofer Publica [http://publica.fraunhofer.de/oai.har] (Germany)}, |
604
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4139 crossref = {JMLR-shorter}, |
498
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4140 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. |
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4141 |
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4142 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.} |
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4143 } |
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4144 |
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4145 @ARTICLE{Takeuchi-Bengio-Kanamori-2002, |
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4146 author = {Takeuchi, Ichiro and Bengio, Yoshua and Kanamori, Takafumi}, |
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4147 title = {Robust Regression with Asymmetric Heavy-Tail Noise Distributions}, |
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4148 journal = {Neural Computation}, |
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4149 volume = {14}, |
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4150 number = {10}, |
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4151 year = {2002}, |
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4152 pages = {2469--2496}, |
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4153 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.}, |
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4154 topics={Mining},cat={J}, |
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4155 } |
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4156 |
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4157 @PHDTHESIS{ThesisChapados2010, |
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4158 author = {Chapados, Nicolas}, |
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4159 title = {Sequential Machine learning Approaches for Portfolio Management}, |
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4160 year = {2010}, |
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4161 school = {Universit{\'{e}} de Montr{\'{e}}al}, |
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4162 abstract = {[English follow] |
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4163 Cette th{\`{e}}se envisage un ensemble de m{\'{e}}thodes permettant aux algorithmes d'apprentissage statistique de mieux traiter la nature s{\'{e}}quentielle des probl{\`{e}}mes de gestion de portefeuilles financiers. Nous d{\'{e}}butons par une consid{\'{e}}ration du probl{\`{e}}me g{\'{e}}n{\'{e}}ral de la composition d'algorithmes d'apprentissage devant g{\'{e}}rer des t{\^{a}}ches s{\'{e}}quentielles, en particulier celui de la mise-{\`{a}}-jour efficace des ensembles d'apprentissage dans un cadre de validation s{\'{e}}quentielle. Nous {\'{e}}num{\'{e}}rons les desiderata que des primitives de composition doivent satisfaire, et faisons ressortir la difficult{\'{e}} de les atteindre de fa{\c c}on rigoureuse et efficace. Nous poursuivons en pr{\'{e}}sentant un ensemble d'algorithmes qui atteignent ces objectifs et pr{\'{e}}sentons une {\'{e}}tude de cas d'un syst{\`{e}}me complexe de prise de d{\'{e}}cision financi{\`{e}}re utilisant ces techniques. Nous d{\'{e}}crivons ensuite une m{\'{e}}thode g{\'{e}}n{\'{e}}rale permettant de transformer un probl{\`{e}}me de d{\'{e}}cision s{\'{e}}quentielle non-Markovien en un probl{\`{e}}me d'apprentissage supervis{\'{e}} en employant un algorithme de recherche bas{\'{e}} sur les K meilleurs chemins. Nous traitons d'une application en gestion de portefeuille o{\`{u}} nous entra{\^{\i}}nons un algorithme d'apprentissage {\`{a}} optimiser directement un ratio de Sharpe (ou autre crit{\`{e}}re non-additif incorporant une aversion au risque). Nous illustrons l'approche par une {\'{e}}tude exp{\'{e}}rimentale approfondie, proposant une architecture de r{\'{e}}seaux de neurones sp{\'{e}}cialis{\'{e}}e {\`{a}} la gestion de portefeuille et la comparant {\`{a}} plusieurs alternatives. Finalement, nous introduisons une repr{\'{e}}sentation fonctionnelle de s{\'{e}}ries chronologiques permettant {\`{a}} des pr{\'{e}}visions d'{\^{e}}tre effectu{\'{e}}es sur un horizon variable, tout en utilisant un ensemble informationnel r{\'{e}}v{\'{e}}l{\'{e}} de mani{\`{e}}re progressive. L'approche est bas{\'{e}}e sur l'utilisation des processus Gaussiens, lesquels fournissent une matrice de covariance compl{\`{e}}te entre tous les points pour lesquels une pr{\'{e}}vision est demand{\'{e}}e. Cette information est utilis{\'{e}}e {\`{a}} bon escient par un algorithme qui transige activement des {\'{e}}carts de cours (price spreads) entre des contrats {\`{a}} terme sur commodit{\'{e}}s. L'approche propos{\'{e}}e produit, hors {\'{e}}chantillon, un rendement ajust{\'{e}} pour le risque significatif, apr{\`{e}}s frais de transactions, sur un portefeuille de 30 actifs. |
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4164 This thesis considers a number of approaches to make machine learning algorithms better suited to the sequential nature of financial portfolio management tasks. We start by considering the problem of the general composition of learning algorithms that must handle temporal learning tasks, in particular that of creating and efficiently updating the training sets in a sequential simulation framework. We enumerate the desiderata that composition primitives should satisfy, and underscore the difficulty of rigorously and efficiently reaching them. We follow by introducing a set of algorithms that accomplish the desired objectives, presenting a case-study of a real-world complex learning system for financial decision-making that uses those techniques. We then describe a general method to transform a non-Markovian sequential decision problem into a supervised learning problem using a K-best paths search algorithm. We consider an application in financial portfolio management where we train a learning algorithm to directly optimize a Sharpe Ratio (or other risk-averse non-additive) utility function. We illustrate the approach by demonstrating extensive experimental results using a neural network architecture specialized for portfolio management and compare against well-known alternatives. Finally, 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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4165 } |
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4166 |
498
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4167 @ARTICLE{Thierry+al-2008, |
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4168 author = {Bertin-Mahieux, Thierry and Eck, Douglas and Maillet, Fran{\c c}ois and Lamere, Paul}, |
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4169 title = {Autotagger: A Model For Predicting Social Tags from Acoustic Features on Large Music Databases}, |
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4170 journal = {Journal of New Music Research}, |
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4171 year = {2008}, |
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4172 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 |
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4173 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.} |
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4174 } |
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4175 |
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4176 @ARTICLE{Thivierge+al-2007, |
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4177 author = {Thivierge, J. -P. and Rivest, Fran{\c c}ois and Monchi, O}, |
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4178 title = {Spiking Neurons, Dopamine, and Plasticity: Timing Is Everything, But Concentration Also Matters}, |
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4179 journal = {Synapse}, |
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4180 volume = {61}, |
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4181 year = {2007}, |
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4182 pages = {375-390}, |
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4183 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.} |
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4184 } |
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4185 |
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4186 @TECHREPORT{tonga-tr, |
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4187 author = {Le Roux, Nicolas and Manzagol, Pierre-Antoine and Bengio, Yoshua}, |
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4188 title = {Topmoumoute online natural gradient algorithm}, |
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4189 number = {1299}, |
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4190 year = {2007}, |
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4191 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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4192 abstract = {Guided by the goal of obtaining an optimization algorithm that is |
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4193 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.} |
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4194 } |
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4195 |
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4196 @TECHREPORT{TR1197, |
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4197 author = {Vincent, Pascal and Bengio, Yoshua}, |
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4198 title = {K-Local Hyperplane and Convex Distance Nearest Neighbor Algorithms}, |
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4199 number = {1197}, |
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4200 year = {2001}, |
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4201 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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4202 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/TR1197.pdf}, |
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4203 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.}, |
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4204 topics={Kernel},cat={T}, |
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4205 } |
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4206 |
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4207 @TECHREPORT{TR1198, |
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4208 author = {Takeuchi, Ichiro and Bengio, Yoshua and Kanamori, Takafumi}, |
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4209 title = {Robust Regression with Asymmetric Heavy-Tail Noise}, |
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4210 number = {1198}, |
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4211 year = {2001}, |
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4212 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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4213 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/TR1198.pdf}, |
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4214 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.}, |
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4215 topics={Mining},cat={T}, |
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4216 } |
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4217 |
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4218 @TECHREPORT{TR1199, |
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4219 author = {Chapados, Nicolas and Bengio, Yoshua and Vincent, Pascal and Ghosn, Joumana and Dugas, Charles and Takeuchi, Ichiro and Meng, Linyan}, |
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4220 title = {Estimating Car Insurance Premia: a Case Study in High-Dimensional Data Inference}, |
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4221 number = {1199}, |
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4222 year = {2001}, |
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4223 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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4224 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/TR1199.pdf}, |
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4225 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.}, |
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4226 topics={HighDimensional,Mining},cat={T}, |
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4227 } |
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4228 |
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4229 @TECHREPORT{TR1200, |
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4230 author = {Bengio, Yoshua and Chapados, Nicolas}, |
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4231 title = {Extending Metric-Based Model Selection and Regularization in the Absence of Unlabeled Data}, |
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4232 number = {1200}, |
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4233 year = {2001}, |
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4234 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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4235 url = {http://www.iro.umontreal.ca/lisa/pointeurs/TR1200.ps}, |
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4236 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.}, |
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4237 topics={ModelSelection,Finance},cat={T}, |
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4238 } |
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4239 |
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4240 @TECHREPORT{TR1215, |
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4241 author = {Bengio, Yoshua}, |
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4242 title = {New Distributed Probabilistic Language Models}, |
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4243 number = {1215}, |
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4244 year = {2002}, |
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4245 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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4246 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/TR1215.ps}, |
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4247 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.}, |
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4248 topics={Markov,Language,Unsupervised},cat={T}, |
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4249 } |
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4250 |
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4251 @TECHREPORT{TR1216, |
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4252 author = {Bengio, Yoshua and S{\'{e}}n{\'{e}}cal, Jean-S{\'{e}}bastien}, |
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4253 title = {Quick Training of Probabilistic Neural Nets by Importance Sampling}, |
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4254 number = {1216}, |
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4255 year = {2002}, |
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4256 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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4257 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/tr1216.ps}, |
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4258 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.}, |
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4259 topics={Markov,Language,Unsupervised},cat={T}, |
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4260 } |
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4261 |
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4262 @TECHREPORT{TR1231, |
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4263 author = {Bengio, Yoshua and Kermorvant, Christopher}, |
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4264 title = {Extracting Hidden Sense Probabilities from Bitexts}, |
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4265 number = {1231}, |
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4266 year = {2003}, |
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4267 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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4268 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/TR1231.pdf}, |
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4269 abstract = {We propose a probabilistic model that is inspired by Diab & Resniks algorithm to extract disambiguation information from aligned bilingual texts. Like Diab & Resniks, 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 inWordNets ontology. Unlike Diab & Resniks algorithm it does not suppose that the translation in the source language has a single meaning.}, |
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4270 topics={Language},cat={T}, |
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4271 } |
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4272 |
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4273 @TECHREPORT{TR1232, |
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4274 author = {Bengio, Yoshua and Vincent, Pascal and Paiement, Jean-Fran{\c c}ois}, |
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4275 title = {Learning Eigenfunctions of Similarity: Linking Spectral Clustering and Kernel {PCA}}, |
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4276 number = {1232}, |
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4277 year = {2003}, |
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4278 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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4279 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/TR1232.pdf}, |
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4280 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.}, |
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4281 topics={HighDimensional,Kernel,Unsupervised},cat={T}, |
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4282 } |
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4283 |
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4284 @TECHREPORT{TR1234, |
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4285 author = {Bengio, Yoshua and Grandvalet, Yves}, |
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4286 title = {No Unbiased Estimator of the Variance of K-Fold Cros-Validation}, |
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4287 number = {1234}, |
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4288 year = {2003}, |
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4289 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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4290 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/TR1234.pdf}, |
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4291 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 dont 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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4292 topics={Comparative},cat={T}, |
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4293 } |
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4294 |
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4295 @TECHREPORT{tr1238, |
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4296 author = {Bengio, Yoshua and Paiement, Jean-Fran{\c c}ois and Vincent, Pascal}, |
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4297 title = {Out-of-Sample Extensions for {LLE}, {I}somap, {MDS}, {E}igenmaps, and Spectral Clustering}, |
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4298 number = {1238}, |
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4299 year = {2003}, |
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4300 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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4301 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/tr1238.pdf}, |
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4302 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.}, |
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4303 topics={HighDimensional,Kernel,Unsupervised},cat={T}, |
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4304 } |
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4305 |
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4306 @TECHREPORT{tr1239, |
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4307 author = {Bengio, Yoshua and Vincent, Pascal and Paiement, Jean-Fran{\c c}ois and Delalleau, Olivier and Ouimet, Marie and Le Roux, Nicolas}, |
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4308 title = {Spectral Clustering and Kernel {PCA} are Learning Eigenfunctions}, |
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4309 number = {1239}, |
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4310 year = {2003}, |
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4311 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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4312 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/tr1239.pdf}, |
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4313 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.}, |
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4314 topics={HighDimensional,Kernel,Unsupervised},cat={T}, |
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4315 } |
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4316 |
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4317 @TECHREPORT{tr1240, |
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4318 author = {Vincent, Pascal and Bengio, Yoshua}, |
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4319 title = {Locally Weighted Full Covariance Gaussian Density Estimation}, |
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4320 number = {1240}, |
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4321 year = {2003}, |
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4322 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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4323 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/tr1240.pdf}, |
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4324 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.}, |
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4325 topics={HighDimensional,Kernel,Unsupervised},cat={T}, |
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4326 } |
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4327 |
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4328 @TECHREPORT{tr1247, |
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4329 author = {Bengio, Yoshua and Delalleau, Olivier and Le Roux, Nicolas}, |
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4330 title = {Efficient Non-Parametric Function Induction in Semi-Supervised Learning}, |
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4331 number = {1247}, |
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4332 year = {2004}, |
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4333 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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4334 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/tr1247.pdf}, |
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4335 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.}, |
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4336 topics={Kernel,Unsupervised},cat={T}, |
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4337 } |
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4338 |
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4339 @TECHREPORT{tr1250, |
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4340 author = {Bengio, Yoshua and Monperrus, Martin}, |
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4341 title = {Discovering shared structure in manifold learning}, |
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4342 number = {1250}, |
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4343 year = {2004}, |
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4344 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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4345 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/tr-tangent.pdf}, |
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4346 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.}, |
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4347 topics={HighDimensional,Kernel,Unsupervised},cat={T}, |
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4348 } |
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4349 |
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4350 @TECHREPORT{tr1252, |
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4351 author = {Bengio, Yoshua and Larochelle, Hugo}, |
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4352 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}, |
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4353 number = {1252}, |
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4354 year = {2004}, |
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4355 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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4356 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/tr1252.pdf}, |
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4357 abstract = {La d{\'{e}}sambigu{\"{\i}}sation s{\'{e}}mantique est un sujet qui suscite beaucoup dint{\'{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 dexamples dapprentissage, ses performances sont comparables {\`{a}} celles de lalgorithme de Bayes na{\"{\i}}f. Il pourrait {\'{e}}ventuellement {\^{e}}tre adapt{\'{e}} {\`{a}} des corpus bi-textes.}, |
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4358 topics={Unsupervised,Language},cat={T}, |
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4359 } |
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4360 |
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4361 @TECHREPORT{tr1281, |
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4362 author = {Le Roux, Nicolas and Bengio, Yoshua}, |
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4363 title = {Continuous Neural Networks}, |
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4364 number = {1281}, |
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4365 year = {2006}, |
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4366 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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4367 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/continuous_nnet_tr1281.pdf}, |
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4368 abstract = {This article extends neural networks to the case of an uncountable number of hidden units, in several |
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4369 ways. In the first approach proposed, a finite parametrization is possible, allowing gradient-based |
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4370 learning. While having the same number of parameters as an ordinary neural network, its internal |
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4371 structure suggests that it can represent some smooth functions much more compactly. Under mild |
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4372 assumptions, we also find better error bounds than with ordinary neural networks. Furthermore, this |
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4373 parametrization may help reducing the problem of saturation of the neurons. In a second approach, the |
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4374 input-to-hidden weights are fully non-parametric, yielding a kernel machine for which we demonstrate |
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4375 a simple kernel formula. Interestingly, the resulting kernel machine can be made hyperparameter-free |
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4376 and still generalizes in spite of an absence of explicit regularization.}, |
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4377 cat={T},topics={Kernel,HighDimensional}, |
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4378 } |
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4379 |
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4380 @TECHREPORT{tr1282, |
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4381 author = {Bengio, Yoshua and Lamblin, Pascal and Popovici, Dan and Larochelle, Hugo}, |
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4382 title = {Greedy Layer-Wise Training of Deep Networks}, |
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4383 number = {1282}, |
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4384 year = {2006}, |
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4385 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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4386 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/dbn_supervised_tr1282.pdf}, |
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4387 abstract = {Deep multi-layer neural networks have many levels of non-linearities, which allows them to potentially |
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4388 represent very compactly highly non-linear and highly-varying functions. However, until recently it |
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4389 was not clear how to train such deep networks, since gradient-based optimization starting from random |
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4390 initialization appears to often get stuck in poor solutions. Hinton et al. recently introduced a greedy |
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4391 layer-wise unsupervised learning algorithm for Deep Belief Networks (DBN), a generative model with |
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4392 many layers of hidden causal variables. In the context of the above optimization problem, we study |
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4393 this algorithm empirically and explore variants to better understand its success and extend it to cases |
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4394 where the inputs are continuous or where the structure of the input distribution is not revealing enough |
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4395 about the variable to be predicted in a supervised task.}, |
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4396 cat={T},topics={HighDimensional,Unsupervised}, |
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4397 } |
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4398 |
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4399 @TECHREPORT{tr1283, |
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4400 author = {Carreau, Julie and Bengio, Yoshua}, |
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4401 title = {A Hybrid {Pareto} Model for Asymmetric Fat-Tail Data}, |
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4402 number = {1283}, |
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4403 year = {2006}, |
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4404 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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4405 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/fat_tails_tr1283.pdf}, |
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4406 abstract = {We propose an estimator for the conditional density p(Y |X) that can adapt for asymmetric heavy tails |
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4407 which might depend on X. Such estimators have important applications in finance and insurance. We |
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4408 draw from Extreme Value Theory the tools to build a hybrid unimodal density having a parameter |
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4409 controlling the heaviness of the upper tail. This hybrid is a Gaussian whose upper tail has been |
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4410 replaced by a generalized {Pareto} tail. We use this hybrid in a multi-modal mixture in order to obtain |
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4411 a nonparametric density estimator that can easily adapt for heavy tailed data. To obtain a conditional |
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4412 density estimator, the parameters of the mixture estimator can be seen as functions of X and these |
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4413 functions learned. We show experimentally that this approach better models the conditional density in |
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4414 terms of likelihood than compared competing algorithms: conditional mixture models with other types |
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4415 of components and multivariate nonparametric models.}, |
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4416 cat={T},topics={Unsupervised,Mining}, |
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4417 } |
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4418 |
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4419 @TECHREPORT{tr1284, |
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4420 author = {Larochelle, Hugo and Bengio, Yoshua}, |
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4421 title = {Distributed Representation Prediction for Generalization to New Words}, |
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4422 number = {1284}, |
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4423 year = {2006}, |
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4424 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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4425 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/dist_rep_pred_tr1284.pdf}, |
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4426 abstract = {Learning distributed representations of symbols (e.g. words) has been used in several Natural Language Processing |
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4427 systems. Such representations can capture semantic or syntactic similarities between words, which permit to fight |
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4428 the curse of dimensionality when considering sequences of such words. Unfortunately, because these representations |
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4429 are learned only for a previously determined vocabulary of words, it is not clear how to obtain representations |
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4430 for new words. We present here an approach which gets around this problem by considering the distributed representations |
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4431 as predictions from low-level or domain-knowledge features of words. We report experiments on a Part |
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4432 Of Speech tagging task, which demonstrates the success of this approach in learning meaningful representations and |
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4433 in providing improved accuracy, especially for new words.}, |
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4434 cat={T},topics={HighDimensional,Language}, |
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4435 } |
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4436 |
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4437 @TECHREPORT{tr1285, |
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4438 author = {Grandvalet, Yves and Bengio, Yoshua}, |
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4439 title = {Hypothesis Testing for Cross-Validation}, |
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4440 number = {1285}, |
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4441 year = {2006}, |
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4442 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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4443 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/xv_rho_stat_tr1285.pdf}, |
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4444 abstract = {K-fold cross-validation produces variable estimates, whose variance cannot be estimated unbiasedly. However, in practice, one would like to |
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4445 provide a figure related to the variability of this estimate. The first part |
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4446 of this paper lists a series of restrictive assumptions (on the distribution of |
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4447 cross-validation residuals) that allow to derive unbiased estimates. We exhibit three such estimates, corresponding to differing assumptions. Their |
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4448 similar expressions however entail almost identical empirical behaviors. |
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4449 Then, we look for a conservative test for detecting significant differences |
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4450 in performances between two algorithms. Our proposal is based on the |
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4451 derivation of the form of a t-statistic parametrized by the correlation of |
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4452 residuals between each validation set. Its calibration is compared to the |
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4453 usual t-test. While the latter is overconfident in concluding that differences are indeed significant, our test is bound to be more skeptical, with |
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4454 smaller type-I error.}, |
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4455 cat={T},topics={ModelSelection,Comparative}, |
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4456 } |
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4457 |
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4458 @TECHREPORT{tr1286, |
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4459 author = {Erhan, Dumitru and Bengio, Yoshua and {L'Heureux}, Pierre-Jean and Yue, Shi Yi}, |
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4460 title = {Generalizing to a Zero-Data Task: a Computational Chemistry Case Study}, |
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4461 number = {1286}, |
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4462 year = {2006}, |
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4463 institution = {D{\'{e}}partement d'informatique et recherche op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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4464 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/mt_qsar_tr1286.pdf}, |
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4465 abstract = {We investigate the problem of learning several tasks simultaneously in order to transfer the acquired |
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4466 knowledge to a completely new task for which no training data are available. Assuming that the tasks |
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4467 share some representation that we can discover efficiently, such a scenario should lead to a better model of |
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4468 the new task, as compared to the model that is learned by only using the knowledge of the new task. We |
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4469 have evaluated several supervised learning algorithms in order to discover shared representations among |
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4470 the tasks defined in a computational chemistry/drug discovery problem. We have cast the problem from |
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4471 a statistical learning point of view and set up the general hypotheses that have to be tested in order |
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4472 to validate the multi-task learning approach. We have then evaluated the performance of the learning |
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4473 algorithms and showed that it is indeed possible to learn a shared representation of the tasks that allows |
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4474 to generalize to a new task for which no training data are available. From a theoretical point of view, |
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4475 our contribution also comprises a modification to the Support Vector Machine algorithm, which can |
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4476 produce state-of-the-art results using multi-task learning concepts at its core. From a practical point |
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4477 of view, our contribution is that this algorithm can be readily used by pharmaceutical companies for |
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4478 virtual screening campaigns.}, |
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4479 cat={T},topics={MultiTask,Kernel,Bioinformatic}, |
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4480 } |
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4481 |
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4482 @INPROCEEDINGS{Turian+al-2009, |
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4483 author = {Turian, Joseph and Bergstra, James and Bengio, Yoshua}, |
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4484 title = {Quadratic Features and Deep Architectures for Chunking}, |
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4485 booktitle = {North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL HLT)}, |
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4486 year = {2009}, |
604
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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583
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4487 pages = {245--248}, |
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Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
583
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|
4488 publisher = {Association for Computational Linguistics}, |
51213beaed8b
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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583
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4489 url = {http://www.aclweb.org/anthology/N/N09/N09-2062}, |
498
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4490 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.} |
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4491 } |
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4492 |
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4493 @INPROCEEDINGS{Turian+al-2010, |
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4494 author = {Turian, Joseph and Ratinov, Lev and Bengio, Yoshua and Roth, Dan}, |
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4495 title = {A preliminary evaluation of word representations for named-entity recognition}, |
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4496 booktitle = {NIPS Workshop on Grammar Induction, Representation of Language and Language Learning}, |
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4497 year = {2009}, |
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4498 url = {http://www.iro.umontreal.ca/~lisa/pointeurs/wordrepresentations-ner.pdf}, |
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4499 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.} |
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4500 } |
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4501 |
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4502 @INPROCEEDINGS{Turian+Ratinov+Bengio-2010, |
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4503 author = {Turian, Joseph and Ratinov, Lev and Bengio, Yoshua}, |
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4504 title = {Word representations: A simple and general method for semi-supervised learning}, |
604
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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4505 booktitle = {Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics(ACL2010)}, |
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parents:
583
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4506 year = {2010}, |
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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583
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4507 pages = {384--394}, |
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Yoshua Bengio <bengioy@iro.umontreal.ca>
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4508 publisher = {Association for Computational Linguistics}, |
498
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4509 } |
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4510 |
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4511 @INPROCEEDINGS{Vincent-Bengio-2003, |
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4512 author = {Vincent, Pascal and Bengio, Yoshua}, |
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4513 title = {Manifold Parzen Windows}, |
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4514 year = {2003}, |
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4515 pages = {825--832}, |
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4516 crossref = {NIPS15-shorter}, |
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4517 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.}, |
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4518 topics={HighDimensional,Kernel,Unsupervised},cat={C}, |
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4519 } |
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4520 |
604
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4521 @ARTICLE{Vincent-JMLR-2010, |
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583
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4522 author = {Vincent, Pascal and Larochelle, Hugo and Lajoie, Isabelle and Bengio, Yoshua and Manzagol, Pierre-Antoine}, |
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4523 title = {Stacked Denoising Autoencoders: learning useful representations in a deep network with a local denoising criterion}, |
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583
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4524 journal = {JMLR}, |
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4525 year = {2010}, |
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583
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|
4526 note = {to appear} |
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4527 } |
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4528 |
498
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4529 @TECHREPORT{Vincent-TR1316, |
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4530 author = {Vincent, Pascal and Larochelle, Hugo and Bengio, Yoshua and Manzagol, Pierre-Antoine}, |
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4531 title = {Extracting and Composing Robust Features with Denoising Autoencoders}, |
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4532 number = {1316}, |
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4533 year = {2008}, |
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4534 institution = {D{\'{e}}partement d'Informatique et Recherche Op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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4535 url = {http://www.iro.umontreal.ca/~vincentp/Publications/denoising_autoencoders_tr1316.pdf}, |
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4536 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.} |
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4537 } |
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4538 |
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4539 @PHDTHESIS{Vincent2003, |
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4540 author = {Vincent, Pascal}, |
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4541 title = {Mod{\`{e}}les {\`{a}} Noyaux {\`{a}} Structure Locale}, |
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4542 year = {2003}, |
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4543 school = {D{\'{e}}partement d'Informatique et Recherche Op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
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4544 } |
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4545 |
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4546 @ARTICLE{vincent:2001, |
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4547 author = {Vincent, Pascal and Bengio, Yoshua}, |
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4548 title = {Kernel Matching Pursuit}, |
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4549 journal = {Machine Learning}, |
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4550 year = {2001}, |
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4551 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.}, |
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4552 topics={HighDimensional,Kernel},cat={J}, |
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4553 } |
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4554 |
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4555 @INPROCEEDINGS{VincentPLarochelleH2008, |
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4556 author = {Vincent, Pascal and Larochelle, Hugo and Bengio, Yoshua and Manzagol, Pierre-Antoine}, |
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4557 title = {Extracting and Composing Robust Features with Denoising Autoencoders}, |
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4558 year = {2008}, |
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4559 pages = {1096--1103}, |
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4560 crossref = {ICML08-shorter}, |
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4561 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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4562 for deep feed-forward neural network classifiers, and are not considered as a standalone solution to classification problems. In |
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4563 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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4564 RBMs which aim at introducing a discriminative component to RBM training and improve their performance as classifiers. This |
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4565 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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4566 } |
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4567 |
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4568 @TECHREPORT{visualization_techreport, |
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4569 author = {Erhan, Dumitru and Bengio, Yoshua and Courville, Aaron and Vincent, Pascal}, |
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4570 title = {Visualizing Higher-Layer Features of a Deep Network}, |
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4571 number = {1341}, |
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4572 year = {2009}, |
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4573 institution = {University of Montreal}, |
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4574 abstract = {Deep architectures have demonstrated state-of-the-art results in a variety of |
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4575 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} |
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4576 } |
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4577 |
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4578 @INPROCEEDINGS{xAISTATS2009-short, |
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4579 title = {Proc. AISTATS'2009}, |
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4580 booktitle = {Proc. AISTATS'2009}, |
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4581 year = {2009} |
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4582 } |
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4583 |
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4584 |
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4585 @MISC{Yoshua+al-snowbird-2008, |
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4586 author = {Bengio, Yoshua and Larochelle, Hugo and Turian, Joseph}, |
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4587 title = {Deep Woods}, |
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4588 year = {2008}, |
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4589 howpublished = {Poster presented at the Learning@Snowbird Workshop, Snowbird, USA, 2008} |
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4590 } |
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4591 |
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4592 @ARTICLE{Zaccaro-et-al-2005, |
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4593 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}, |
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4594 title = {Selective Small Molecule Peptidomimetic Ligands of TrkC and TrkA Receptors Afford Discrete or Complete Neurotrophic Activities}, |
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4595 journal = {Chemistry \& Biology}, |
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4596 volume = {12}, |
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4597 number = {9}, |
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4598 year = {2005}, |
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4599 pages = {1015--1028} |
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4600 } |
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4601 |
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4602 |
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4603 |
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4604 crossreferenced publications: |
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4605 @INPROCEEDINGS{ICML09, |
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4606 editor = {Bottou, {L{\'{e}}on} and Littman, Michael}, |
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4607 title = {Proceedings of the Twenty-sixth International Conference on Machine Learning (ICML'09)}, |
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4608 booktitle = {Proceedings of the Twenty-sixth International Conference on Machine Learning (ICML'09)}, |
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4609 year = {-1}, |
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4610 publisher = {ACM} |
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4611 } |
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4612 |
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4613 @INPROCEEDINGS{NIPS7, |
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4614 editor = {Tesauro, G. and Touretzky, D. S. and Leen, T. K.}, |
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4615 title = {Advances in Neural Information Processing Systems 7 (NIPS'94)}, |
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4616 booktitle = {Advances in Neural Information Processing Systems 7 (NIPS'94)}, |
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4617 year = {-1}, |
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4618 publisher = {MIT Press} |
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4619 } |
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4620 |
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4621 @INPROCEEDINGS{NIPS6, |
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4622 editor = {Cowan, J. D. and Tesauro, G. and Alspector, J.}, |
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4623 title = {Advances in Neural Information Processing Systems 6 (NIPS'93)}, |
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4624 booktitle = {Advances in Neural Information Processing Systems 6 (NIPS'93)}, |
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4625 year = {-1}, |
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4626 publisher = {MIT Press} |
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4627 } |
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4628 |
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4629 @INPROCEEDINGS{NIPS8, |
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4630 editor = {Touretzky, D. S. and Mozer, M. and Hasselmo, M.E.}, |
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4631 title = {Advances in Neural Information Processing Systems 8 (NIPS'95)}, |
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4632 booktitle = {Advances in Neural Information Processing Systems 8 (NIPS'95)}, |
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4633 year = {-1}, |
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4634 publisher = {MIT Press} |
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4635 } |
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4636 |
604
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583
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4637 @ARTICLE{JMLR, |
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583
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4638 journal = {Journal of Machine Learning Research}, |
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583
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4639 year = {-1} |
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583
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|
4640 } |
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4641 |
498
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4642 @INPROCEEDINGS{NIPS19, |
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4643 editor = {{Sch{\"{o}}lkopf}, Bernhard and Platt, John and Hoffman, Thomas}, |
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4644 title = {Advances in Neural Information Processing Systems 19 (NIPS'06)}, |
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4645 booktitle = {Advances in Neural Information Processing Systems 19 (NIPS'06)}, |
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4646 year = {-1}, |
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4647 publisher = {MIT Press} |
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4648 } |
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4649 |
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Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4650 @INPROCEEDINGS{NIPS10, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4651 editor = {Jordan, M.I. and Kearns, M.J. and Solla, S.A.}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4652 title = {Advances in Neural Information Processing Systems 10 (NIPS'97)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4653 booktitle = {Advances in Neural Information Processing Systems 10 (NIPS'97)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4654 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4655 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4656 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4657 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4658 @INPROCEEDINGS{NIPS1, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4659 editor = {Touretzky, D. S.}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4660 title = {Advances in Neural Information Processing Systems 1 (NIPS'88)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4661 booktitle = {Advances in Neural Information Processing Systems 1 (NIPS'88)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4662 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4663 publisher = {Morgan Kaufmann} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4664 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4665 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4666 @INPROCEEDINGS{NIPS2, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4667 editor = {Touretzky, D. S.}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4668 title = {Advances in Neural Information Processing Systems 2 (NIPS'89)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4669 booktitle = {Advances in Neural Information Processing Systems 2 (NIPS'89)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4670 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4671 publisher = {Morgan Kaufmann} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4672 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4673 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4674 @INPROCEEDINGS{NIPS4, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4675 editor = {Moody, J. E. and Hanson, S. J. and Lipmann, R. P.}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4676 title = {Advances in Neural Information Processing Systems 4 (NIPS'91)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4677 booktitle = {Advances in Neural Information Processing Systems 4 (NIPS'91)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4678 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4679 publisher = {Morgan Kaufmann} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4680 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4681 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4682 @INPROCEEDINGS{NIPS12, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4683 editor = {Solla, S.A. and Leen, T. K.}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4684 title = {Advances in Neural Information Processing Systems 12 (NIPS'99)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4685 booktitle = {Advances in Neural Information Processing Systems 12 (NIPS'99)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4686 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4687 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4688 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4689 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4690 @INPROCEEDINGS{NIPS16, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4691 editor = {Becker, S. and Saul, L. and {Sch{\"{o}}lkopf}, Bernhard}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4692 title = {Advances in Neural Information Processing Systems 16 (NIPS'03)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4693 booktitle = {Advances in Neural Information Processing Systems 16 (NIPS'03)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4694 year = {-1} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4695 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4696 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4697 @INPROCEEDINGS{NIPS22, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4698 editor = {Bengio, Yoshua and Schuurmans, Dale and Williams, Christopher and Lafferty, John and Culotta, Aron}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4699 title = {Advances in Neural Information Processing Systems 22 (NIPS'09)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4700 booktitle = {Advances in Neural Information Processing Systems 22 (NIPS'09)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4701 year = {-1} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4702 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4703 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4704 @INPROCEEDINGS{NIPS20, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4705 editor = {Platt, John and Koller, D. and Singer, Yoram and Roweis, S.}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4706 title = {Advances in Neural Information Processing Systems 20 (NIPS'07)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4707 booktitle = {Advances in Neural Information Processing Systems 20 (NIPS'07)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4708 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4709 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4710 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4711 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4712 @INPROCEEDINGS{xAISTATS2009, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4713 title = {Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS 2009)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4714 booktitle = {Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS 2009)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4715 year = {2009}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4716 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4717 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4718 @INPROCEEDINGS{NIPS9, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4719 editor = {Mozer, M. and Jordan, M.I. and Petsche, T.}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4720 title = {Advances in Neural Information Processing Systems 9 (NIPS'96)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4721 booktitle = {Advances in Neural Information Processing Systems 9 (NIPS'96)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4722 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4723 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4724 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4725 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4726 @INPROCEEDINGS{NIPS17, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4727 editor = {Saul, Lawrence K. and Weiss, Yair and Bottou, {L{\'{e}}on}}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4728 title = {Advances in Neural Information Processing Systems 17 (NIPS'04)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4729 booktitle = {Advances in Neural Information Processing Systems 17 (NIPS'04)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4730 year = {-1} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4731 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4732 |
604
51213beaed8b
draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
583
diff
changeset
|
4733 @PROCEEDINGS{ICML08, |
498
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4734 editor = {Cohen, William W. and McCallum, Andrew and Roweis, Sam T.}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4735 title = {Proceedings of the Twenty-fifth International Conference on Machine Learning (ICML'08)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4736 booktitle = {Proceedings of the Twenty-fifth International Conference on Machine Learning (ICML'08)}, |
604
51213beaed8b
draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
583
diff
changeset
|
4737 year = {2008}, |
498
7ff00c27c976
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Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4738 publisher = {ACM} |
7ff00c27c976
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Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4739 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4740 |
604
51213beaed8b
draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
583
diff
changeset
|
4741 @PROCEEDINGS{ICML07, |
498
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4742 editor = {Ghahramani, Zoubin}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4743 title = {Proceedings of the 24th International Conference on Machine Learning (ICML'07)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4744 booktitle = {Proceedings of the 24th International Conference on Machine Learning (ICML'07)}, |
604
51213beaed8b
draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
583
diff
changeset
|
4745 year = {2007}, |
498
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4746 publisher = {ACM} |
7ff00c27c976
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Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4747 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4748 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4749 @TECHREPORT{DIRO, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4750 title = {DIRO}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4751 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4752 institution = {D{\'{e}}partement d'Informatique et de Recherche Op{\'{e}}rationnelle, Universit{\'{e}} de Montr{\'{e}}al}, |
7ff00c27c976
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Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4753 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4754 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4755 @INPROCEEDINGS{NIPS18, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4756 editor = {Weiss, Yair and {Sch{\"{o}}lkopf}, Bernhard and Platt, John}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4757 title = {Advances in Neural Information Processing Systems 18 (NIPS'05)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4758 booktitle = {Advances in Neural Information Processing Systems 18 (NIPS'05)}, |
7ff00c27c976
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Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4759 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4760 publisher = {MIT Press} |
7ff00c27c976
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Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4761 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4762 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4763 @INPROCEEDINGS{NIPS13, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4764 editor = {Leen, T. K. and Dietterich, T.G.}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4765 title = {Advances in Neural Information Processing Systems 13 (NIPS'00)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4766 booktitle = {Advances in Neural Information Processing Systems 13 (NIPS'00)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4767 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4768 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4769 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4770 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4771 @INPROCEEDINGS{ICML05, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4772 editor = {Raedt, Luc De and Wrobel, Stefan}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4773 title = {Proceedings of the Twenty-second International Conference on Machine Learning (ICML'05)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4774 booktitle = {Proceedings of the Twenty-second International Conference on Machine Learning (ICML'05)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4775 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4776 publisher = {ACM} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4777 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4778 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4779 @INPROCEEDINGS{ICML06, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4780 editor = {Cohen, William W. and Moore, Andrew}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4781 title = {Proceedings of the Twenty-three International Conference on Machine Learning (ICML'06)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4782 booktitle = {Proceedings of the Twenty-three International Conference on Machine Learning (ICML'06)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4783 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4784 publisher = {ACM} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4785 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4786 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4787 @INPROCEEDINGS{NIPS15, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4788 editor = {Becker, S. and Thrun, Sebastian}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4789 title = {Advances in Neural Information Processing Systems 15 (NIPS'02)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4790 booktitle = {Advances in Neural Information Processing Systems 15 (NIPS'02)}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4791 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4792 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4793 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4794 |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4795 @INPROCEEDINGS{ICML01-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4796 title = {ICML'01}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4797 booktitle = {ICML'01}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4798 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4799 publisher = {Morgan Kaufmann} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4800 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4801 @INPROCEEDINGS{ICML02-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4802 title = {ICML'02}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4803 booktitle = {ICML'02}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4804 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4805 publisher = {Morgan Kaufmann} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4806 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4807 @INPROCEEDINGS{ICML03-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4808 title = {ICML'03}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4809 booktitle = {ICML'03}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4810 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4811 publisher = {AAAI Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4812 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4813 @INPROCEEDINGS{ICML04-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4814 title = {ICML'04}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4815 booktitle = {ICML'04}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4816 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4817 publisher = {ACM} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4818 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4819 @INPROCEEDINGS{ICML05-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4820 title = {ICML'05}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4821 booktitle = {ICML'05}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4822 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4823 publisher = {ACM} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4824 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4825 @INPROCEEDINGS{ICML06-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4826 title = {ICML'06}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4827 booktitle = {ICML'06}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4828 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4829 publisher = {ACM} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4830 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4831 @INPROCEEDINGS{ICML07-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4832 title = {ICML'07}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4833 booktitle = {ICML'07}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4834 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4835 publisher = {ACM} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4836 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4837 @INPROCEEDINGS{ICML08-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4838 title = {ICML'08}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4839 booktitle = {ICML'08}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4840 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4841 publisher = {ACM} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4842 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4843 @INPROCEEDINGS{ICML09-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4844 title = {ICML'09}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4845 booktitle = {ICML'09}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4846 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4847 publisher = {ACM} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4848 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4849 @INPROCEEDINGS{ICML96-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4850 title = {ICML'96}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4851 booktitle = {ICML'96}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4852 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4853 publisher = {Morgan Kaufmann} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4854 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4855 @INPROCEEDINGS{ICML97-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4856 title = {ICML'97}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4857 booktitle = {ICML'97}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4858 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4859 publisher = {Morgan Kaufmann} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4860 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4861 @INPROCEEDINGS{ICML98-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4862 title = {ICML'98}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4863 booktitle = {ICML'98}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4864 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4865 publisher = {Morgan Kaufmann} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4866 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4867 @INPROCEEDINGS{ICML99-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4868 title = {ICML'99}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4869 booktitle = {ICML'99}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4870 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4871 publisher = {Morgan Kaufmann} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4872 } |
604
51213beaed8b
draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
583
diff
changeset
|
4873 @ARTICLE{JMLR-shorter, |
51213beaed8b
draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
583
diff
changeset
|
4874 journal = {JMLR}, |
51213beaed8b
draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
583
diff
changeset
|
4875 year = {-1} |
51213beaed8b
draft of NIPS 2010 workshop camera-ready version
Yoshua Bengio <bengioy@iro.umontreal.ca>
parents:
583
diff
changeset
|
4876 } |
498
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4877 @INPROCEEDINGS{NIPS1-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4878 title = {NIPS'88}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4879 booktitle = {NIPS 1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4880 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4881 publisher = {Morgan Kaufmann} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4882 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4883 @INPROCEEDINGS{NIPS10-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4884 title = {NIPS'97}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4885 booktitle = {NIPS 10}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4886 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4887 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4888 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4889 @INPROCEEDINGS{NIPS11-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4890 title = {NIPS'98}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4891 booktitle = {NIPS 11}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4892 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4893 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4894 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4895 @INPROCEEDINGS{NIPS12-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4896 title = {NIPS'99}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4897 booktitle = {NIPS 12}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4898 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4899 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4900 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4901 @INPROCEEDINGS{NIPS13-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4902 title = {NIPS'00}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4903 booktitle = {NIPS 13}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4904 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4905 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4906 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4907 @INPROCEEDINGS{NIPS14-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4908 title = {NIPS'01}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4909 booktitle = {NIPS 14}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4910 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4911 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4912 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4913 @INPROCEEDINGS{NIPS15-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4914 title = {NIPS'02}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4915 booktitle = {NIPS 15}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4916 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4917 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4918 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4919 @INPROCEEDINGS{NIPS16-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4920 title = {NIPS'03}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4921 booktitle = {NIPS 16}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4922 year = {-1} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4923 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4924 @INPROCEEDINGS{NIPS17-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4925 title = {NIPS'04}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4926 booktitle = {NIPS 17}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4927 year = {-1} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4928 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4929 @INPROCEEDINGS{NIPS18-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4930 title = {NIPS'05}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4931 booktitle = {NIPS 18}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4932 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4933 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4934 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4935 @INPROCEEDINGS{NIPS19-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4936 title = {NIPS'06}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4937 booktitle = {NIPS 19}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4938 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4939 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4940 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4941 @INPROCEEDINGS{NIPS2-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4942 title = {NIPS'89}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4943 booktitle = {NIPS 2}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4944 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4945 publisher = {Morgan Kaufmann} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4946 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4947 @INPROCEEDINGS{NIPS20-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4948 title = {NIPS'07}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4949 booktitle = {NIPS 20}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4950 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4951 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4952 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4953 @INPROCEEDINGS{NIPS21-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4954 title = {NIPS'08}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4955 booktitle = {NIPS 21}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4956 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4957 publisher = {Nips Foundation (http://books.nips.cc)} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4958 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4959 @INPROCEEDINGS{NIPS22-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4960 title = {NIPS'09}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4961 booktitle = {NIPS 22}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4962 year = {-1} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4963 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4964 @INPROCEEDINGS{NIPS3-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4965 title = {NIPS'90}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4966 booktitle = {NIPS 3}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4967 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4968 publisher = {Morgan Kaufmann} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4969 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4970 @INPROCEEDINGS{NIPS4-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4971 title = {NIPS'91}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4972 booktitle = {NIPS 4}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4973 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4974 publisher = {Morgan Kaufmann} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4975 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4976 @INPROCEEDINGS{NIPS5-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4977 title = {NIPS'92}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4978 booktitle = {NIPS 5}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4979 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4980 publisher = {Morgan Kaufmann} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4981 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4982 @INPROCEEDINGS{NIPS6-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4983 title = {NIPS'93}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4984 booktitle = {NIPS 6}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4985 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4986 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4987 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4988 @INPROCEEDINGS{NIPS7-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4989 title = {NIPS'94}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4990 booktitle = {NIPS 7}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4991 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4992 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4993 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4994 @INPROCEEDINGS{NIPS8-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4995 title = {NIPS'95}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4996 booktitle = {NIPS 8}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4997 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4998 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
4999 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
5000 @INPROCEEDINGS{NIPS9-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
5001 title = {NIPS'96}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
5002 booktitle = {NIPS 9}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
5003 year = {-1}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
5004 publisher = {MIT Press} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
5005 } |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
5006 @INPROCEEDINGS{xAISTATS2009-shorter, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
5007 title = {AISTATS'2009}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
5008 booktitle = {AISTATS'2009}, |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
5009 year = {-1} |
7ff00c27c976
add missing file for bibtex and make it smaller.
Frederic Bastien <nouiz@nouiz.org>
parents:
diff
changeset
|
5010 } |