Mercurial > ift6266
view writeup/ml.bib @ 449:7bdd412754ea
Added support to calculate the human consensus error
| author | humel |
|---|---|
| date | Sun, 09 May 2010 15:01:31 -0400 |
| parents | a6d339033d03 |
| children | d48a7777e4d8 |
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%%WARNING: READ THE README FILE BEFORE ANY MODIFICATION!!! %%submitted papers %%% @Article{Bergstra+Bengio+Louradoj-2008sub, author = "J. Bergstra and Y. Bengio and J. Louradour", title = "Suitability of Complex Cell Models for Object Categorization", journal = "Computational Neuroscience", year = "2008", note = "Rejected." } @Article{Bergstra+Bengio+Louradoj-2009sub, author = "J. Bergstra and Y. Bengio and J. Louradour", title = "Suitability of Complex Cell Models for Object Categorization", journal = "Neural Computation", year = "2009", note = "Submitted." } @Article{Chapados+Bengio-2008sub, author = "N. Chapados and Y. Bengio", title = "Forecasting and Trading Commodity Contract Spreads with {G}aussian Processes", journal = "International Journal of Forecasting", year = "2008", note = "Submitted.", } @Article{Chapados+Bengio-2008sub2, author = "N. Chapados and Y. Bengio", title = "Training Graphs of Learning Modules for Sequential Data", journal = "ACM Transactions on Knowledge Discovery from Data", year = "2008", note = "Submitted.", } %%% %%accepted or published papers %%% @Article{Grother, author = "Grother Patrick J.", title = "NIST special database. Handprinted forms and characters database", publisher = "National institute of standards and technology", year = "1995" } @InCollection{Trentin+al-2002, author = "E. Trentin and F. Brugnara and Y. Bengio and C. Furlanello and R. De Mori", editor = "R. Daniloff", booktitle = "Connectionist Approaches to Clinical Problems in Speech and Language", title = "Statistical and Neural Network Models for Speech Recognition", publisher = "Lawrence Erlbaum", pages = "213--264", year = "2002", } @InCollection{Bengio+grandvalet-2004, author = "Y. Bengio and Y. Grandvalet", editor = "P. Duchesne and B. Remillard", booktitle = "Statistical Modeling and Analysis for Complex Data Problem", title = "Bias in Estimating the Variance of K-Fold Cross-Validation", publisher = "Lawrence Erlbaum", address = "Kluwer", pages = "75--95", year = "2004", } @InCollection{Dugas+al-2004, author = "C. Dugas and Y. Bengio and N. Chapados and P. Vincent and G. Denoncourt and C. Fournier", editor = "L. Jain and A.F. Shapiro", booktitle = "Intelligent and Other Computational Techniques in Insurance: Theory and Applications", title = "Statistical Learning Algorithms Applied to Automobile Insurance Ratemaking", publisher = "World Scientific Publishing Company", year = "2004", } @InCollection{Dugas+al-2004-short, author = "C. Dugas and Y. Bengio and N. Chapados and P. Vincent and G. Denoncourt and C. Fournier", booktitle = "Intelligent and Other Computational Techniques in Insurance: Theory and Applications", title = "Statistical Learning Algorithms Applied to Automobile Insurance Ratemaking", publisher = "World Scientific Publishing Company", year = "2004", } @inproceedings{Collobert+Bengio+Bengio-2002b, author = "R. Collobert and Y. Bengio and S. Bengio", title = {Scaling Large Learning Problems with Hard Parallel Mixtures}, editor = "S.W. Lee and A. Verri", year = 2002, booktitle = SVM02, volume = "2388 of Lecture Notes in Computer Science", publisher = "Springer-Verlag", pages = "8--23", } @Article{Collobert+Bengio+Bengio-2003, author = "R. Collobert and Y. Bengio and S. Bengio.", title = "Scaling Large Learning Problems with Hard Parallel Mixtures", journal = ijprai, volume = "17", number = "3", pages = "349--365", year = "2003", } @Article{Collobert+Bengio+Bengio-2003-small, author = "R. Collobert and Y. Bengio and S. 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DSI 11/94", institution = "Universit\`a di Firenze", year = "1994", } @article{Bengio-nc-2004, author = {Yoshua Bengio and Olivier Delalleau and Nicolas Le Roux and Jean-François Paiement and Pascal Vincent and Marie Ouimet}, title = {Learning eigenfunctions links spectral embedding and kernel {PCA}}, journal = {Neural Computation}, volume = 16, number = 10, year = 2004, pages = {2197--2219}, } @article{Bengio-nc-2004-small, author = {Yoshua Bengio and Olivier Delalleau and Nicolas Le Roux and Jean-François Paiement and Pascal Vincent and Marie Ouimet}, title = {{\small{Learning eigenfunctions links spectral embedding and kernel {PCA}}}}, journal = {Neural Comp.}, volume = {16(10)}, year = 2004, pages = {2197--2219}, } @Article{Bengio+Grandvalet-JMLR-2004, author = "Yoshua Bengio and Yves Grandvalet", title = "No Unbiased Estimator of the Variance of {K}-Fold Cross-Validation", journal = jmlr, volume = "5", pages = "1089--1105", year = "2004", } @TechReport{Bengio+Grandvalet-TR-2003, author = "Yoshua Bengio and Yves Grandvalet", title = "No Unbiased Estimator of the Variance of {K}-Fold Cross-Validation", number = "TR-2003-1234", institution = "Universite de Montreal, dept. IRO", year = "2003", } @InCollection{Bengio+Lecun-chapter2007, author = "Yoshua Bengio and Yann {LeCun}", editor = "L. Bottou and O. Chapelle and D. DeCoste and J. Weston", booktitle = "Large Scale Kernel Machines", title = "Scaling Learning Algorithms towards {AI}", publisher = "MIT Press", year = "2007", } @InCollection{Bengio+Lecun-chapter2007-small, author = "Y. Bengio and Y. {LeCun}", booktitle = "Large Scale Kernel Machines", title = "Scaling Learning Algorithms towards {AI}", year = "2007", } @InProceedings{Bengio+LeCun94b, author = "Yoshua Bengio and Yann {LeCun}", booktitle = ICPR94, title = "Word Normalization For On-Line Handwritten Word Recognition", pages = "409--413", year = "1994", } @Article{Bengio+Monperrus+Larochelle-2006, author = "Yoshua Bengio and Martin Monperrus and Hugo Larochelle", title = "Nonlocal Estimation of Manifold Structure", journal = "Neural Computation", volume = "18", number = "10", pages = "2509--2528", year = "2006", } @InProceedings{Bengio+Monperrus-2005, author = "Yoshua Bengio and Martin Monperrus", editor = NIPS17ed, booktitle = NIPS17, title = "Non-Local Manifold Tangent Learning", publisher = "{MIT} Press", year = "2005", pages = "129--136", url = "http://www.iro.umontreal.ca/~lisa/pointeurs/tangent\_learner\_nips2004.pdf", } @InProceedings{Bengio+Senecal-2003-small, author = "Yoshua Bengio and Jean-S\'ebastien Sen\'ecal", booktitle = "Proceedings of AISTATS 2003", title = "Quick Training of Probabilistic Neural Nets by Importance Sampling", year = "2003", } @TechReport{Bengio+Vincent+Paiement-TR2003, author = "Yoshua Bengio and Pascal Vincent and Jean-Fran{\cc}ois Paiement", title = "Learning Eigenfunctions of Similarity: Linking Spectral Clustering and Kernel {PCA}", number = "1232", institution = "D\'epartement d'informatique et recherche op\'erationnelle, Universit\'e de Montr\'eal", year = "2003", URL = "www.iro.umontreal.ca/~lisa/pointeurs/TR1232.pdf", } @TechReport{Bengio-decision-trees-TR-2007, author = "Yoshua Bengio and Olivier Delalleau and Clarence Simard", title = "Trees do not Generalize to New Variations", number = "", institution = "D\'epartement d'informatique et recherche op\'erationnelle, Universit\'e de Montr\'eal", year = "2007", } @TechReport{Bengio-decision-trees07, author = "Yoshua Bengio and Olivier Delalleau and Clarence Simard", title = "Decision Trees do not Generalize to New Variations", number = "1304", institution = "Universite de Montreal, Dept. IRO", year = "2007", url = "http://www.iro.umontreal.ca/~lisa/pointeurs/bengio+al-tr1304.pdf", } %I deprecate the following one as this is a duplicate of the preceding tech report! %Their was only one .tex file that was using it. I modified it. @TechReport{Bengio-Trees-TR2007, author = "Yoshua Bengio and Olivier Delalleau and Clarence Simard", title = "Decision Trees do not Generalize to New Variations", number = "1304", institution = "Dept. IRO, Universit\'e de Montr\'eal", year = "2007", url = "http://www.iro.umontreal.ca/~lisa/pointeurs/bengio+al-tr1304.pdf", } @Article{Bengio-hmms99, author = "Yoshua Bengio", title = "Markovian Models for Sequential Data", journal = "Neural Computing Surveys", volume = "2", pages = "129--162", year = "1999", } @Article{bengio-hyper-NC00, author = "Yoshua Bengio", title = "Gradient-Based Optimization of Hyperparameters", journal = "Neural Computation", volume = "12", number = "8", pages = "1889--1900", year = "2000", } @TechReport{bengio-hyper-TR98, author = "Yoshua Bengio", title = "Continuous Optimization of Hyper-Parameters for Non-{IID} Data", institution = "D\'epartement d'informatique et recherche op\'erationnelle, Universit\'e de Montr\'eal", year = "1998", note = "unpublished manuscript", } @Article{Bengio-Hyper-Weight-Decay-nips, author = "Simon Latendresse and Yoshua Bengio", title = "Linear Regression and the Optimization of Hyper-Parameters", journal = "submitted to NIPS'99", year = "1999", } @TechReport{Bengio-Hyper-Weight-Decay-TR, author = "Yoshua Bengio and Simon Latendresse", title = "Soft Variable Selection with Numerical Optimization of Weight Decays", institution = "D\'epartement d'informatique et recherche op\'erationnelle, Universit\'e de Montr\'eal", year = "1999", note = "in preparation", } @Article{Bengio-ijns97, author = "Yoshua Bengio", title = "Using a Financial Training Criterion Rather than a Prediction Criterion", journal = "International Journal of Neural Systems", year = "1997", volume = {8}, number = {4}, note = "Special issue on noisy time-series", pages = {433--443}, URL = "www.iro.umontreal.ca/~lisa/pointeurs/profitcost.ps", } @Article{Bengio-IEEETRNN-2001, author = "Yoshua Bengio and Vincent-Philippe Lauzon and R\'ejean Ducharme", title = "Experiments on the Application of {IOHMM}s to Model Financial Returns Series", journal = ieeetrnn, volume = 12, number = 1, pages = {113--123}, year = "2001", } @InProceedings{Bengio-Larochelle-NLMP-NIPS-2006, author = "Yoshua Bengio and Hugo Larochelle and Pascal Vincent", editor = NIPS18ed, booktitle = NIPS18, title = "Non-Local Manifold Parzen Windows", publisher = "MIT Press", pages = "115--122", year = "2006", } @TechReport{Bengio-Larochelle-NLMP-TR-2005, author = "Yoshua Bengio and Hugo Larochelle", title = "Non-Local Manifold Parzen Windows", number = "1264", institution = "D\'epartement d'informatique et recherche op\'erationnelle, Universit\'e de Montr\'eal", year = "2005", } %have been rejected and later accepted to NIPS in Bengio-localfailure-NIPS-2006 @InProceedings{Bengio-localfailure-icml-2005, author = "Yoshua Bengio and Olivier Delalleau and Nicolas {Le Roux}", booktitle = "submitted to ICML 2005", title = "The Curse of Dimensionality for Local Kernel Machines", year = "2005", } @InCollection{Bengio-localfailure-NIPS-2006, author = "Yoshua Bengio and Olivier Delalleau and Nicolas {Le Roux}", editor = NIPS18ed, booktitle = NIPS18, title = "The Curse of Highly Variable Functions for Local Kernel Machines", publisher = "{MIT} Press", address = "Cambridge, MA", pages = "107--114", year = "2006", } @InCollection{Bengio-localfailure-NIPS-2006-small, author = "Yoshua Bengio and Olivier Delalleau and Nicolas {Le Roux}", booktitle = "NIPS 18", title = "The Curse of Highly Variable Functions for Local Kernel Machines", publisher = "{MIT} Press", address = "Cambridge, MA", pages = "107--114", year = "2006", } @InProceedings{Bengio-localfailure-snowbird-2005, author = "Yoshua Bengio and Olivier Delalleau and Nicolas {Le Roux}", booktitle = "The Learning Workshop", title = "The Curse of Dimensionality for Local Kernel Machines", address = "Snowbird, Utah", year = "2005", } @InProceedings{HonglakLee-2007, author = "Honglak Lee and Alexis Battle and Rajat Raina and Andrew Ng", editor = NIPS19ed, booktitle = NIPS19, title = "Efficient sparse coding algorithms", publisher = "MIT Press", pages = "801--808", year = "2007", } @InProceedings{Bengio-nips-2006-small, author = "Y. Bengio and P. Lamblin and D. Popovici and H. Larochelle", booktitle = "Advances in NIPS 19", title = "Greedy Layer-Wise Training of Deep Networks", year = "2007", } @InProceedings{Bengio-nips-2006-short, author = "Y. Bengio and P. Lamblin and D. Popovici and H. Larochelle", booktitle = "Adv. Neural Inf. Proc. Sys. 19", title = "Greedy Layer-Wise Training of Deep Networks", pages = "153--160", year = "2007", } @InProceedings{Bengio-nips2004, author = "Yoshua Bengio and Jean-Fran\c{cois} Paiement and Pascal Vincent and Olivier Delalleau and Nicolas {Le Roux} and Marie Ouimet", editor = NIPS16ed, booktitle = NIPS16, title = "Out-of-Sample Extensions for {LLE}, {Isomap}, {MDS}, {Eigenmaps}, and {Spectral} {Clustering}", publisher = "MIT Press", year = "2004", } @InProceedings{Bengio-nips2003, author = "Yoshua Bengio and Jean-Fran\c{cois} Paiement and Pascal Vincent and Olivier Delalleau and Nicolas {Le Roux} and Marie Ouimet", editor = NIPS16ed, booktitle = NIPS16, title = "Out-of-Sample Extensions for {LLE}, {Isomap}, {MDS}, {Eigenmaps}, and {Spectral} {Clustering}", publisher = "MIT Press", year = "2004", } @InCollection{Bengio-NIPS2007, author = "Yoshua Bengio and Pascal Lamblin and Dan Popovici and Hugo Larochelle", editor = NIPS19ed, booktitle = NIPS19, title = "Greedy Layer-Wise Training of Deep Networks", publisher = "MIT Press", pages = "153--160", year = "2007", } @InProceedings{Bengio-nnlm2001, author = "Yoshua Bengio and R{\'e}jean Ducharme and Pascal Vincent", editor = NIPS13ed, booktitle = NIPS13, title = "A Neural Probabilistic Language Model", publisher = "{MIT} Press", pages = "933--938", year = "2001", url = "http://www.iro.umontreal.ca/~lisa/pointeurs/nips00-lm.ps", } @Article{Bengio-nnlm2003, author = "Yoshua Bengio and R{\'e}jean Ducharme and Pascal Vincent and Christian Jauvin", title = "A Neural Probabilistic Language Model", journal = jmlr, volume = "3", pages = "1137--1155", year = "2003", } @Article{Bengio-nnlm2003-small, author = "Y. Bengio and R. Ducharme and P. Vincent and C. Jauvin", title = "A Neural Probabilistic Language Model", journal = "JMLR", volume = "3", pages = "1137--1155", year = "2003", } @Article{Bengio-NonStat-Hyper-ML, author = "Yoshua Bengio and Charles Dugas", title = "Learning Simple Non-Stationarities with Hyper-Parameters", journal = "submitted to Machine Learning", year = "1999", } @Article{Bengio-prel92, author = "Y. Bengio and M. Gori and R. \mbox{De Mori}", title = "Learning the Dynamic Nature of Speech with Back-propagation for Sequences", journal = prel, volume = "13", number = "5", pages = "375--385", year = "1992", note = "(Special issue on Artificial Neural Networks)", } @Article{Bengio-2008, author = "Yoshua Bengio", title = "Learning Deep Architectures for {AI}", journal = {Foundations and Trends in Machine Learning}, year = "2009", volume = {to appear}, } @Article{Bengio-2009-short, author = "Y. Bengio", title = "Learning Deep Architectures for {AI}", journal = {Foundations \& Trends in Mach. Learn.}, year = "2009", volume = 2, number = 1, pages = {1--127}, } @TechReport{Bengio-TR1312-small, author = "Yoshua Bengio", title = "Learning Deep Architectures for {AI}", number = "1312", institution = "U. Montr\'eal, dept. IRO", year = "2007", } @InProceedings{Bengio-transducers-98, author = "Y. Bengio and S. Bengio and J. F. Isabelle and Y. Singer", editor = NIPS10ed, booktitle = NIPS10, title = "Shared Context Probabilistic Transducers", publisher = "MIT Press", pages = "409--415", year = "1998", } @Article{Bengio-trnn92, author = "Y. Bengio and R. \mbox{De Mori} and G. Flammia and R. Kompe", title = "Global Optimization of a Neural Network-Hidden {Markov} Model Hybrid", journal = ieeetrnn, volume = "3", number = "2", pages = "252--259", year = "1992", } @Article{Bengio-trnn93, author = "Y. Bengio and P. Simard and P. 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De Mori", editor = NIPS1ed, booktitle = NIPS1, title = "Use of multi-layered networks for coding speech with phonetic features", publisher = "Morgan Kaufmann, San Mateo", address = "Denver, CO", pages = "224--231", year = "1989", } @PhdThesis{Bengio91, author = "Yoshua Bengio", title = "Artificial Neural Networks and their Application to Sequence Recognition", school = "McGill University, (Computer Science)", address = "Montreal, Qc., Canada", year = "1991", } @InProceedings{bengio91x, author = "Y. Bengio and R. {De Mori} and G. Flammia and R. Kompe", booktitle = ijcnn, title = "Global Optimization of a Neural Network - Hidden Markov Model Hybrid", volume = "2", pages = "789--794", year = "1991", OPTaddress = "Seattle WA", } @article{Becker92, author = {Sue Becker and Geoffrey Hinton}, title = {A self-organizing neural network that discovers surfaces in random-dot stereograms}, journal = {Nature}, volume = 355, pages = {161--163}, year = 1992, } @Article{Bengio93, author = "Yoshua Bengio", title = "A Connectionist Approach to Speech Recognition", journal = "International Journal on Pattern Recognition and Artificial Intelligence", volume = "7", number = "4", pages = "647--668", note = "special issue entitled Advances in Pattern Recognition Systems using Neural Networks", year = "1993", } @InProceedings{Bengio93e, author = "S. Bengio and Y. Bengio and J. Cloutier and J. Gecsei", editor = "S. Gielen and B. Kappen", booktitle = "Proceedings of the International Conference on Artificial Neural Networks 1993", title = "Generalization of a Parametric Learning Rule", publisher = "Springer-Verlag", address = "Amsterdam, The Netherlands", pages = "502--502", year = "1993", } @Article{bengio:1999:nc, author = "S. Bengio and Y. Bengio and J. Robert and G. B\'elanger", title = "Stochastic Learning of Strategic Equilibria for Auctions", journal = "Neural Computation", volume = "11", number = "5", pages = "1199--1209", year = "1999", } @Article{bottou+al:1999, author = "L. Bottou and P. Haffner and P.G. Howard and P. Simard and Y. Bengio", title = "High quality document image compression with {DjVu}", journal = "Journal of Electronic Imaging", volume = "7", number = "3", pages = "410--425", year = "1998", } @Article{bengio+al:1998, author = "Y. Bengio and F. Gingras and B. Goulard and J.-M. Lina", title = "Gaussian Mixture Densities for Classification of Nuclear Power Plant Data", journal = "Computers and Artificial Intelligence, special issue on Intelligent Technologies for Electric and Nuclear Power Plants", volume = "17", number = "2--3", pages = "189--209", year = "1998", } @Article{GingrasBengio:1998, author = "F. Gingras and Y. Bengio", title = "Handling Asynchronous or Missing Financial Data with Recurrent Networks", journal = "International Journal of Computational Intelligence and Organizations", volume = "1", number = "3", pages = "154--163", year = "1998", } @Article{BengioS95, author = "S. Bengio and Y. Bengio and J. Cloutier", title = "On the search for new learning rules for {ANN}s", journal = "Neural Processing Letters", volume = "2", number = "4", pages = "26--30", year = "1995", } @Article{BengioMori89, author = "Y. Bengio and R. De Mori", title = "Use of multilayer networks for the recognition of phonetic features and phonemes", journal = "Computational Intelligence", volume = "5", pages = "134--141", year = "1989", } @TechReport{BengioTR1178, author = "Yoshua Bengio and R\'ejean Ducharme and Pascal Vincent", title = "A Neural Probabilistic Language Model", number = "1178", institution = "Dept. IRO, Universit\'e de Montr\'eal", year = "2002", } @TechReport{BengioTR1215, author = "Yoshua Bengio", title = "New Distributed Probabilistic Language Models", number = "1215", institution = "Dept. IRO, Universit\'e de Montr\'eal", year = "2002", } @Book{Bengio_book96, author = "Yoshua Bengio", title = "Neural Networks for Speech and Sequence Processing", publisher = "International Thomson Computer Press", year = "1996", } @InProceedings{Bengio_icnn93, author = "Y. Bengio and P. Frasconi and P. Simard", booktitle = icnn, title = "The problem of learning long-term dependencies in recurrent networks", publisher = "IEEE Press", address = "San Francisco", pages = "1183--1195", year = "1993", note = "(invited paper)", } @Article{Bengio_trnn94, author = "Y. Bengio and P. Simard and P. Frasconi", title = "Learning Long-Term Dependencies with Gradient Descent is Difficult", journal = ieeetrnn, volume = "5", number = "2", pages = "157--166", year = "1994", note = "Special Issue on Recurrent Neural Networks, March 94", } @Book{Benveniste90, author = "A. Benveniste and M. Metivier and P. Priouret", title = "Adaptive Algorithms and Stochastic Approximations", publisher = "Springer-Verlag", address = "Berlin, New York", year = "1990", } @Book{Berger85, author = "J. Berger", title = "Statistical Decision Theory and {Bayesian} Analysis", publisher = "Springer", year = "1985", } @Misc{berger97improved, author = "A. 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Ritov", title = "Inference in hidden {Markov} models {I}: local asymptotic normality in the stationary case", number = "Technical Report 383", institution = "Statistics Department, University of California, Berkeley", year = "February 1994, revised April 1995", } @Article{Bienenstock82, author = "E. L. Bienenstock and L. N. Cooper and P. W. Munro", title = "Theory for the Development of Neuron Selectivity: Orientation Specificity and Binocular Interaction in Visual Cortex", journal = jneuro, volume = "2", year = "1982", } @Article{BierdermanI1987, author = "Irving Bierderman", title = "Recognition-by-Components: {A} Theory of Human Image Understanding", journal = "Psychological Review", volume = "94", number = "2", publisher = "American Psychological Association, Inc.", pages = "115--147", year = "1987", added-by = "Daniel Acevedo", date-added = "Thu Oct 24 12:45:17 2002", project = "genetic", theme = "perception and vr and tech and natural and medicine and art", } @InProceedings{Bilbro89a, author = "G. Bilbro and R. Mann and T. K. Miller and W. E. Snyder and D. E. Van den Bout and M. White", editor = NIPS1ed, booktitle = NIPS1, title = "Optimization by Mean Field Annealing", publisher = "Morgan Kaufmann, San Mateo", address = "Denver, CO", pages = "91--98", year = "1989", } @InProceedings{Bilbro89b, author = "G. L. 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Bishop", title = "Pattern Recognition and Machine Learning", publisher = "Springer", year = "2006", } @Book{bishop-book95, author = "Christopher Bishop", title = "Neural Networks for Pattern Recognition", publisher = "Oxford University Press", address = "London, UK", year = "1995", } @Article{bishop92, author = "Christopher Bishop", title = "Exact calculation of the {Hessian} matrix for the multi-layer perceptron", journal = "Neural Computation", volume = "4", number = "4", pages = "494--501", year = "1992", } @Article{bishop95training, author = "Christopher M. Bishop", title = "Training with Noise is Equivalent to {Tikhonov} Regularization", journal = "Neural Computation", volume = "7", number = "1", pages = "108--116", year = "1995", } @Article{Blackscholes73, author = "F. Black and M. Scholes", title = "The Pricing of Options and Corporate Liabilities", journal = "Journal of Political Economy", number = "81", pages = "637--654", year = "1973", } @Article{Blakemore70, author = "C. 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Bottou and F. Fogelman-Souli\'e and P. Blanchet and J. S. Lienard", key = "bottou", title = "Speaker independent isolated digit recognition: multilayer perceptrons vs dynamic time warping", journal = "Neural Networks", volume = "3", pages = "453--465", year = "1990", } @InProceedings{Bottou91, author = "L. Bottou and P. Gallinari", editor = NIPS3ed, booktitle = NIPS3, title = "A Framework for the Cooperation of Learning Algorithms", address = "Denver, CO", pages = "781--788", year = "1991", } @Article{Bottou92, author = "L. Bottou and V. Vapnik", key = "Bottou92", title = "Local Learning Algorithms", journal = nc, volume = "4", number = "6", pages = "888--900", year = "1992", } @InProceedings{Bottou94, author = "L. Bottou and C. Cortes and J. S. Denker and H. Drucker and I. Guyon and L. D. Jackel and Y. {LeCun} and U. A. Muller and E. Sackinger and P. Simard and V. Vapnik", booktitle = "International Conference on Pattern Recognition", title = "Comparison of classifier methods: a case study in handwritten digit recognition", address = "Jerusalem, Israel", year = "1994", } @InProceedings{Bottou97, author = "L{\'e}on Bottou and Yoshua Bengio and Yann {LeCun}", booktitle = cvpr97, title = "Global Training of Document Processing Systems using Graph Transformer Networks", publisher = "IEEE", address = "Puerto Rico", pages = "490--494", year = "1997", } @InCollection{Bottou98, author = "L{\'e}on Bottou", editor = "David Saad", booktitle = "Online Learning in Neural Networks", title = "Online Algorithms and Stochastic Approximations", publisher = "Cambridge University Press", address = "Cambridge, UK", pages = "", year = "1998", } @PhdThesis{Bottou_these91, author = "L\'eon Bottou", title = "Une approche th\'eorique de l'apprentissage connexioniste; applications \`a la reconnaissance de la parole", school = "Universit\'e de Paris XI", year = "1991", } @InProceedings{BouchardG2004, author = "Guillaume Bouchard and Bill Triggs", booktitle = "IASC International Symposium on Computational Statistics (COMPSTAT)", title = "The Tradeoff Between Generative and Discriminative Classifiers", address = "Prague", pages = "721--728", month = aug, year = "2004", keywords = "LEAR, LAVA", } %URL = "http://lear.inrialpes.fr/pubs/2004/BT04", @inproceedings{BouchardG2007, author = {Guillaume Bouchard}, title = {Bias-Variance Tradeoff in Hybrid Generative-Discriminative Models}, booktitle = ICML07, editor = ICML07ed, publisher = ICML07publ, year = {2007}, isbn = {0-7695-3069-9}, pages = {124--129}, address = {Washington, DC, USA}, } %doi = {http://dx.doi.org/10.1109/ICMLA.2007.23}, @Article{Bourlard-cspla89, author = "H. 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Bengio", %% booktitle = icjnn %% title = "Incorporating Second-Order Functional Knowledge for Better Option Pricing", %% volume = "V", %% pages = "79--84", %% year = "2000", %%} @inproceedings{Bengio2000, title={Probabilistic neural network models for sequential data}, author={Bengio, Y.}, booktitle=ijcnn, year={2000}, volume={5}, pages={79-84}, abstract={Artificial neural networks (ANN) can be incorporated into probabilistic models. In this paper we review some of the approaches which have been proposed to incorporate them into probabilistic models of sequential data, such as hidden Markov models (HMM). 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Sys. 19", title = "Efficient Learning of Sparse Representations with an Energy-Based Model", pages = {1137--1144}, year = "2007", } # Please do NOT use this citation as it is a duplicate of ranzato-07 @InCollection{ranzato-06, author = "{Marc'Aurelio} Ranzato and Christopher Poultney and Sumit Chopra and Yann {LeCun}", editor = NIPS19ed, booktitle = NIPS19, title = "Efficient Learning of Sparse Representations with an Energy-Based Model", publisher = "{MIT} Press", pages = "", year = "2007", } # Please do NOT use this citation as it is a duplicate of ranzato-07-small @InCollection{ranzato-06-small, author = "M. Ranzato and C. Poultney and S. 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Tomita", booktitle = "Proceedings of the Fourth Annual Cognitive Science Conference", title = "Dynamic Construction of Finite-state Automata from Examples Using Hill-Climbing", address = "Ann Arbor, MI", pages = "105--108", year = "1982", } @Book{Tong83, author = "H. Tong", title = "Threshold Models in Nonlinear Time Series Analysis", publisher = "Springer-Verlag", address = "Berlin", year = "1983", } @InProceedings{TongKoller2000, author = "S. Tong and D. Koller", booktitle = "Proceedings of the 17th National Conference on Artificial Intelligence (AAAI)", title = "Restricted Bayes Optimal Classifiers", address = "Austin, Texas", pages = "658--664", year = "2000", } @Article{Torgerson52, author = "W. 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Towell and J. W. Shavlik", editor = NIPS4ed, booktitle = NIPS4, title = "Interpretation of Artificial Neural Networks: Mapping Knowledge-Based Neural Networks into rules", publisher = "Morgan Kaufmann", address = "San Meteo, CA", pages = "977--984", year = "1992", } @InProceedings{Towell-aaai90, author = "G. G. Towell and J. W. Shawlick and M. O. 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The most widely used algorithms for learning what to put in short-term memory, however, take too much time to be feasible or do not work well at all, especially when minimal time lags between inputs and corresponding teacher signals are long. Although theoretically fascinating, they do not provide clear practical advantages over, say, backprop in feedforward networks with limited time windows (see crossreference Chapters 11 and 12). With conventional \&\#034;algorithms based on the computation of the complete gradient\&\#034;, such as \&\#034;Back-Propagation Through Time\&\#034; (BPTT, e.g., [22, 27, 26]) or \&\#034;Real-Time Recurrent Learning\&\#034; (RTRL, e.g., [21]) error signals \&\#034;flowing backwards in time\&\#034; tend to either (1) blow up or (2) vanish: the temporal evolution of the backpropagated error ex}, author = {Hochreiter, Sepp and Informatik, Fakultat F. and Bengio, Yoshua and Frasconi, Paolo and Schmidhuber, Jurgen}, citeulike-article-id = {4450697}, citeulike-linkout-0 = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.24.7321}, keywords = {gradient-descent, long-term-dependencies, rnn}, posted-at = {2009-05-02 00:58:01}, priority = {2}, title = {Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies}, url = {http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.24.7321}, booktitle = "Field Guide to Dynamical Recurrent Networks", editor = "J. 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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.} } @ARTICLE{Bengio2009FTML, author = {Bengio, Yoshua}, title = {Learning deep architectures for {AI}}, journal = FTML, volume = {2}, number = {1}, year = {2009}, pages = {1--127}, note = Bengio2009FTML_note, abstract = {Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need {\insist deep architectures}. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted {Boltzmann} Machines, used to construct deeper models such as Deep Belief Networks.} } @ARTICLE{Bengio1994ITNN, author = {Bengio, Yoshua and Simard, Patrice and Frasconi, Paolo}, title = {Learning Long-Term Dependencies with Gradient Descent is Difficult}, journal = IEEE_trans_NN, volume = {5}, number = {2}, year = {1994}, pages = {157--166}, 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.}, optnote={(Special Issue on Recurrent Neural Networks)},topics={LongTerm},cat={J}, } @article{Kohler1992, abstract = {The QRS complex is the most striking waveform within the electrocardiogram (ECG). Since it reflects the electrical activity within the heart during the ventricular contraction, the time of its occurrence as well as its shape provide much information about the current state of the heart. Due to its characteristic shape it serves as the basis for the automated determination of the heart rate, as an entry point for classification schemes of the cardiac cycle, and often it is also used in ECG data compression algorithms. In that sense, QRS detection provides the fundamentals for almost all automated ECG analysis algorithms. Software QRS detection has been a research topic for more than 30 years. The evolution of these algorithms clearly reflects the great advances in computer technology. Within the last decade many new approaches to QRS detection have been proposed; for example, algorithms from the field of artificial neural networks genetic algorithms wavelet transforms, filter banks as well as heuristic methods mostly based on nonlinear transforms. The authors provide an overview of these recent developments as well as of formerly proposed algorithms}, author = {Kohler, B. U. and Hennig, C. and Orglmeister, R.}, citeulike-article-id = {546409}, citeulike-linkout-0 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=993193}, journal = eng_med_bio, keywords = {detector, ecg\_processing, qrs, qt\_interval, review\_article, rr\_interval}, number = {1}, pages = {42--57}, posted-at = {2007-11-25 20:38:19}, priority = {2}, title = {The principles of software QRS detection}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=993193}, volume = {21}, year = {2002} } @article{Thomas2006, author = {Julien Thomas and Cedric Rose and Francois Charpillet}, title = {A Multi-HMM Approach to ECG Segmentation}, journal = ICTAI06, volume = {0}, issn = {1082-3409}, year = {2006}, pages = {609-616}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICTAI.2006.17}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } @inproceedings{SimardSP03, author = {Patrice Simard and David Steinkraus and John C. Platt}, title = {Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis}, booktitle = {ICDAR}, year = {2003}, pages = {958-962}, ee = {http://csdl.computer.org/comp/proceedings/icdar/2003/1960/02/196020958abs.htm}, crossref = {DBLP:conf/icdar/2003}, bibsource = {DBLP, http://dblp.uni-trier.de} } @proceedings{DBLP:conf/icdar/2003, title = {7th International Conference on Document Analysis and Recognition (ICDAR 2003), 2-Volume Set, 3-6 August 2003, Edinburgh, Scotland, UK}, booktitle = {ICDAR}, publisher = {IEEE Computer Society}, year = {2003}, isbn = {0-7695-1960-1}, bibsource = {DBLP, http://dblp.uni-trier.de} } @inproceedings{SnowEtAl2008, author = {Snow, R. and O'Connor, B. and Jurafsky, D. and Ng, A.}, booktitle = {Proc. Empirical Methods in NLP}, pages = {254--263}, title = {Cheap and Fast -- But is it Good? 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