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<!DOCTYPE html >
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Hugo Larochelle
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<ul id="nav">
<li><a href="index_fr.html" accesskey="a"><em>A</em>ccueil</a></li>
<li><a href="publications_fr.html" accesskey="p" class="active"><em>P</em>ublications</a></li>
<li><a href="university_fr.html" accesskey="u"><em>U</em>niversité</a></li>
<li><a href="links_fr.html" accesskey="l"><em>L</em>iens</a></li>
<li><a href="publications_en.html" accesskey="a"><em>A</em>nglais</a></li>
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<div id="contentnobars">
<h2> Articles de revues scientifiques </h2>
<ul>
<li> <b> The Hanabi Challenge: A New Frontier for AI Research [<a href="https://www.sciencedirect.com/science/article/pii/S0004370219300116">html</a>] [<a href="https://arxiv.org/abs/1902.00506">arXiv</a>] </b><br>
Nolan Bard, Jakob N.Foerster, Sarath Chandar, Neil Burch, Marc Lanctot, H. Francis Song, Emilio Parisotto, Vincent Dumoulin, Subhodeep Moitra, Edward Hughes, Iain Dunning, Shibl Mourad, Hugo Larochelle, Marc G. Bellemare et Michael Bowling,<br>
<i>Artificial Intelligence</i>, 280, 2020<br><br>
<li> <b> Machine Behaviour [<a href="https://www.nature.com/articles/s41586-019-1138-y">html</a>] [<a href="https://www.nature.com/articles/s41586-019-1138-y.pdf">pdf</a>] </b><br>
Iyad Rahwan, Manuel Cebrian, Nick Obradovich, Josh Bongard, Jean-François Bonnefon, Cynthia Breazeal, Jacob W. Crandall, Nicholas A. Christakis, Iain D. Couzin, Matthew O. Jackson, Nicholas R. Jennings, Ece Kamar, Isabel M. Kloumann, Hugo Larochelle, David Lazer, Richard McElreath, Alan Mislove, David C. Parkes, Alex 'Sandy' Pentland, Margaret E. Roberts, Azim Shariff, Joshua B. Tenenbaum et Michael Wellman,<br>
<i>Nature</i>, 568: 477-486, 2019<br><br>
<li> <b> Document Neural Autoregressive Distribution Estimation [<a href="http://jmlr.org/papers/volume18/16-017/16-017.pdf">pdf</a>] </b><br>
Stanislas Lauly, Yin Zheng, Alexandre Allauzen et Hugo Larochelle,<br>
<i>Journal of Machine Learning Research</i>, 18(113): 1-24, 2017<br><br>
<li> <b> Deep learning with coherent nanophotonic circuits [<a href="https://www.nature.com/nphoton/journal/vaop/ncurrent/full/nphoton.2017.93.html">lien</a>] </b><br>
Yichen Shen, Nicholas C. Harris, Scott Skirlo, Mihika Prabhu, Tom Baehr-Jones, Michael Hochberg, Xin Sun, Shijie Zhao, Hugo Larochelle, Dirk Englund et Marin Soljacic,<br>
<i>Nature Photonics</i>, 2017<br><br>
<li> <b> Movie Description [<a href="http://rdcu.be/oN9P">lien</a>] </b><br>
Anna Rohrbach, Atousa Torabi, Marcus Rohrbach, Niket Tandon, Christopher Pal, Hugo Larochelle, Aaron Courville et Bernt Schiele,<br>
<i>International Journal of Computer Vision</i>, 1-27, 2017<br><br>
<li> <b> Brain tumor segmentation with deep neural networks [<a href="http://www.sciencedirect.com/science/article/pii/S1361841516300330">html</a>] [<a href="https://arx\
iv.org/abs/1505.03540">arXiv</a>] </b><br>
Mohammad Havaei, Axel Davy, David Warde-Farley, Antoine Biard, Aaron Courville, Yoshua Bengio, Chris Pal, Pierre-Marc Jodoin et Hugo Larochelle,<br>
<i>Medical Image Analysis</i>, 35: 18-31, 2017<br><br>
<li> <b> Traffic Analytics with Low Frame Rate Videos [<a href="http://ieeexplore.ieee.org/abstract/document/7756357/?reload=true">html</a>] </b><br>
Zhiming Luo, Pierre-Marc Jodoin, Song-Zhi Su, Shao-Zi Li and Hugo Larochelle,<br>
<i>IEEE Transactions on Circuits and Systems for Video Technology</i>, 2016<br><br>
<li> <b> Neural Autoregressive Distribution Estimation [<a href="http://jmlr.org/papers/volume17/16-272/16-272.pdf">pdf</a>] </b><br>
Benigno Uria, Marc-Alexandre Côté, Karol Gregor, Iain Murray et Hugo Larochelle,<br>
<i>Journal of Machine Learning Research</i>, 17(205): 1-37, 2016<br><br>
<li><b>An Infinite Restricted Boltzmann Machine [<a href="http://www.mitpressjournals.org/doi/pdf/10.1162/NECO_a_00848" target=_top>pdf</a>] [<a href="https://arxiv.org/abs/1502.02476">arxiv</a>]</b><br>
Marc-Alexandre Côté et Hugo Larochelle,<br>
<i>Neural Computation</i>, 28(7): 1265-1288, 2016<br><br>
<li> <b> Domain-Adversarial Training of Neural Networks [<a href="http://jmlr.org/papers/volume17/15-239/15-239.pdf">pdf</a>] </b><br>
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand et Victor Lempitsky,<br>
<i>Journal of Machine Learning Research</i>, 17(59): 1-35, 2016<br><br>
<li> <b> Correlational Neural Networks [<a href="http://www.mitpressjournals.org/doi/pdf/10.1162/NECO_a_00801">pdf</a>] [<a href="http://arxiv.org/abs/1504.07225">arxiv</a>] </b><br>
Sarath Chandar, Mitesh M. Khapra, Hugo Larochelle et Balaraman Ravindran,<br>
<i>Neural Computation</i>, 28(2): 286-304, 2016<br><br>
<li> <b>A Deep and Autoregressive Approach for Topic Modeling of Multimodal Data [<a href="http://arxiv.org/abs/1409.3970" target=_top>arxiv</a>]</b><br>
Yin Zheng, Yu-Jin Zhang and Hugo Larochelle,<br>
<i>IEEE Transactions on Pattern Analysis and Machine Intelligence</i>, 38(6): 1056-1069, 2016<br><br>
<li> <b>Within-Brain Classification for Brain Tumor Segmentation [<a href="http://arxiv.org/abs/1510.01344" target=_top>arxiv</a>]</b><br>
Mohammad Havaei, Hugo Larochelle, Philippe Poulin et Pierre-Marc Jodoin,<br>
<i>International Journal of Computer Assisted Radiology and Surgery</i>, 1-12, 2015<br><br>
<li> <b>A Neural Autoregressive Approach to Attention-based Recognition [<a href="publications/preprint_ijcv_2014.pdf" target=_top>pdf</a>]</b><br>
Yin Zheng, Richard Zemel, Yu-Jin Zhang et Hugo Larochelle,<br>
<i>International Journal of Computer Vision</i>, 113(1): 67-79, 2015<br><br>
<li> <b>PhenoVar: a phenotype-driven approach in clinical genomics for the diagnosis of polymalformative syndromes [<a href="http://www.biomedcentral.com/1755-8794/7/22/abstract" target=_top>html</a>]</b><br>
Yannis J Trakadis, Caroline Buote, Jean-François Therriault, Pierre-Étienne Jacques, Hugo Larochelle et Sébastien Lévesque,<br>
<i>BMC Medical Genomics</i>, 7(22), 2014<br><br>
<li> <b>Nonparametric Guidance of Autoencoder Representations using Label Information [<a href="publications/snoek12a.pdf" target=_top>pdf</a>]</b><br>
Jasper Snoek, Ryan P. Adams et Hugo Larochelle,<br>
<i>Journal of Machine Learning Research</i>, 13(Sep): 2567-2588, 2012<br><br>
<li><b>Learning Where to Attend With Deep Architectures for Image Tracking [<a href="http://arxiv.org/pdf/1109.3737v1.pdf" target=_top>pdf</a>] [<a href="http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00312" target=_top>html</a>]</b><br>
Misha Denil, Loris Bazzani, Hugo Larochelle et Nando de Freitas,<br>
<i>Neural Computation</i>, 24(8): 2151-2184, 2012<br><br>
<li> <b> Learning Algorithms for the Classification Restricted Boltzmann Machine [<a href="publications/larochelle12a.pdf" target=_top>pdf</a>]</b><br>
Hugo Larochelle, Michael Mandel, Razvan Pascanu et Yoshua Bengio,<br>
<i>Journal of Machine Learning Research</i>, 13(Mar): 643-669, 2012<br><br>
<li> <b> Detonation Classification from Acoustic Signature with the Restricted Boltzmann Machine [<a href="http://www.iro.umontreal.ca/~lisa/pointeurs/detonation_rbm_coin_2010.pdf" target=_top>pdf</a>]</b><br>
Yoshua Bengio, Nicolas Chapados, Olivier Delalleau, Hugo Larochelle, Xavier Saint-Mleux, Christian Hudon et Jérôme Louradour,<br>
<i>Computational Intelligence</i>, 28(2): 261-288, 2012<br><br>
<li> <b>Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion [<a href="publications/vincent10a.pdf" target=_top>pdf</a>]</b><br>
Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio et Pierre-Antoine Manzagol,<br>
<i>Journal of Machine Learning Research</i>, 11(Dec): 3371-3408, 2010<br><br>
<li><b>Tractable Multivariate Binary Density Estimation
and the Restricted Boltzmann Forest [<a href="publications/NECO-10-09-1100R2-PDF.pdf" target=_top>pdf</a>]</b><br>
Hugo Larochelle, Yoshua Bengio et Joseph Turian,<br>
<i>Neural Computation</i>, 22(9): 2285-2307, 2010<br><br>
<li> <b>Exploring Strategies for Training Deep Neural Networks
[<a href="publications/jmlr-larochelle09a.pdf" target=_top>pdf</a>]</b><br>
Hugo Larochelle, Yoshua Bengio, Jérôme Louradour et Pascal Lamblin,<br>
<i>Journal of Machine Learning Research</i>, 10(Jan): 1-40, 2009<br><br>
<li> <b>Non-Local Estimation of Manifold Structure
[<a href="publications/nc_submission-final.pdf" target=_top>pdf</a>]
[<a href="publications/nc_submission-final.ps" target=_top>ps</a>]</b><br>
Yoshua Bengio, Martin Monperrus et Hugo Larochelle,<br>
<i>Neural Computation</i>, 18(10): 2509-2528, 2006<br>
</ul>
<h2> Articles de conférences </h2>
<ul>
<li> <b> Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples [<a href="https://openreview.net/forum?id=rkgAGAVKPr">html</a>] </b><br>
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol et Hugo Larochelle,<br>
<i>International Conference on Learning Representations</i>, 2020<br><br>
<li> <b> Language GANs Falling Short [<a href="https://openreview.net/forum?id=BJgza6VtPB">html</a>] </b><br>
Massimo Caccia, Lucas Caccia, William Fedus, Hugo Larochelle, Joelle Pineau et Laurent Charlin,<br>
<i>International Conference on Learning Representations</i>, 2020<br><br>
<li> <b> Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction [<a href="https://arxiv.org/abs/1911.12511">arXiv</a>] </b><br>
Vishal Jain, William Fedus, Hugo Larochelle, Doina Precup et Marc G. Bellemare,<br>
<i>AAAI Conference on Artificial Intelligence</i>, 2020<br><br>
<li> <b> InfoBot: Transfer and Exploration via the Information Bottleneck [<a href="https://openreview.net/forum?id=rJg8yhAqKm">html</a>] </b><br>
Anirudh Goyal, Riashat Islam, DJ Strouse, Zafarali Ahmed, Hugo Larochelle, Matthew Botvinick, Sergey Levine et Yoshua Bengio,<br>
<i>International Conference on Learning Representations</i>, 2019<br><br>
<li> <b> Recall Traces: Backtracking Models for Efficient Reinforcement Learning [<a href="https://openreview.net/forum?id=HygsfnR9Ym">html</a>] </b><br>
Anirudh Goyal, Philemon Brakel, William Fedus, Soumye Singhal, Timothy Lillicrap, Sergey Levine, Hugo Larochelle et Yoshua Bengio,<br>
<i>International Conference on Learning Representations</i>, 2019<br><br>
<li> <b> Meta-learning for semi-supervised few-shot classification [<a href="https://openreview.net/forum?id=HJcSzz-CZ">html</a>] </b><br>
Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua Tenenbaum, Hugo Larochelle et Richard Zemel,<br>
<i>International Conference on Learning Representations</i>, 2018<br><br>
<li> <b> A Meta-Learning Perspective on Cold-Start Recommendations for Items [<a href="http://papers.nips.cc/paper/7266-a-meta-learning-perspective-on-cold-start-recommendations-for-items.pdf">pdf</a>]</b> <br>
Manasi Vartak, Arvind Thiagarajan, Conrado Miranda, Jeshua Bratman et Hugo Larochelle,<br>
<i>Advances in Neural Information Processing Systems 30</i>, 2017<br><br>
<li> <b> Modulating early visual processing by language [<a href="https://arxiv.org/pdf/1707.00683.pdf">pdf</a>]</b> <br>
Harm de Vries, Florian Strub, Jérémie Mary, Hugo Larochelle, Olivier Pietquin et Aaron Courville,<br>
<i>Advances in Neural Information Processing Systems 30</i>, 2017<br><br>
<li> <b> Learn to Track: Deep Learning for Tractography [<a href="https://www.biorxiv.org/content/early/2017/06/06/146688.full.pdf">pdf</a>]</b> <br>
Philippe Poulin, Marc-Alexandre Cote, Jean-Christophe Houde, Laurent Petit, Peter Florian Neher, Klaus H. Maier-Hein, Hugo Larochelle et Maxime Descoteaux,<br>
<i>Medical Image Computing and Computer Assisted Intervention</i>, 2017<br><br>
<li> <b> GuessWhat?! Visual object discovery through multi-modal dialogue [<a href="https://arxiv.org/pdf/1611.08481.pdf">pdf</a>] [<a href="https://guesswhat.ai/">html</a>] </b><br>
Harm de Vries, Florian Strub, Sarath Chandar, Olivier Pietquin, Hugo Larochelle et Aaron Courville,<br>
<i>IEEE Conference on Computer Vision and Pattern Recognition</i>, 2017<br><br>
<li> <b> Optimization as a Model for Few-Shot Learning [<a href="https://openreview.net/forum?id=rJY0-Kcll">html</a>] </b><br>
Sachin Ravi et Hugo Larochelle,<br>
<i>International Conference on Learning Representations</i>, 2017<br><br>
<li> <b> Recurrent Mixture Density Network for Spatiotemporal Visual Attention [<a href="https://openreview.net/forum?id=SJRpRfKxx">html</a>] </b><br>
Loris Bazzani, Hugo Larochelle et Lorenzo Torresani,<br>
<i>International Conference on Learning Representations</i>, 2017<br><br>
<li> <b> Autoencoding beyond pixels using a learned similarity metric [<a href="http://jmlr.org/proceedings/papers/v48/larsen16.pdf">pdf</a>] </b><br>
Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle et Ole Winther,<br>
<i>International Conference on Machine Learning</i>, 2016<br><br>
<li> <b> Dynamic Capacity Networks [<a href="http://jmlr.org/proceedings/papers/v48/almahairi16.pdf">pdf</a>] </b><br>
Amjad Almahairi, Nicolas Ballas, Tim Cooijmans, Yin Zheng, Hugo Larochelle et Aaron Courville,<br>
<i>International Conference on Machine Learning</i>, 2016<br><br>
<li> <b> Describing Videos by Exploiting Temporal Structure [<a href="http://arxiv.org/abs/1502.08029">arxiv</a>] </b><br>
Li Yao, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher Pal, Hugo Larochelle et Aaron Courville,<br>
<i>International Conference on Computer Vision</i>, 2015<br><br>
<li> <b> MADE: Masked Autoencoder for Distribution Estimation [<a href="http://www.jmlr.org/proceedings/papers/v37/germain15.pdf">pdf</a>] </b><br>
Mathieu Germain, Karol Gregor, Iain Murray et Hugo Larochelle,<br>
<i>International Conference on Machine Learning</i>, 2015<br><br>
<li> <b> Using a Recursive Neural Network to Learn an Agent's Decision Model for Plan Recognition [<a href="publications/ijcai15.pdf">pdf</a>] </b><br>
Francis Bisson, Hugo Larochelle et Froduald Kabanza,<br>
<i>International Joint Conference on Artificial Intelligence</i>, 2015<br><br>
<li> <b> An Autoencoder Approach to Learning Bilingual Word Representations [<a href="publications/Bilingual_Autoencoder_NIPS.pdf">pdf</a>] </b> <br>
Sarath Chandar, Stanislas Lauly, Hugo Larochelle, Mitesh M. Khapra, Balaraman Ravindran, Vikas Raykar et Amrita Saha,<br>
<i>Advances in Neural Information Processing Systems 27</i>, 2014<br><br>
<li> <b>Sequential Model-Based Ensemble Optimization [<a href="publications/ESMBO.pdf">pdf</a>]</b> <br>
Alexandre Lacoste, Hugo Larochelle, Mario Marchand et François Laviolette,<br>
<i>Uncertainty in Artificial Intelligence</i>, 2014<br><br>
<li> <b>Leveraging user libraries to bootstrap collaborative filtering [<a href="publications/cf_w_si_kdd.pdf">pdf</a>]</b> <br>
Laurent Charlin, Richard Zemel et Hugo Larochelle,<br>
<i>ACM SIGKDD Conference on Knowledge Discovery and Data Mining</i>, 2014<br><br>
<li> <b>Topic Modeling of Multimodal Data: an Autoregressive Approach [<a href="publications/ZhengY2014.pdf">pdf</a>] </b><br>
Yin Zheng, Yu-Jin Zhang et Hugo Larochelle,<br>
<i>IEEE Conference on Computer Vision and Pattern Recognition</i>, 2014<br><br>
<li> <b>Efficient interactive brain tumor segmentation as within-brain kNN classification [<a href="publications/ICPR_2014_paper.pdf">pdf</a>]</b> <br>
Mohammad Havaei, Pierre-Marc Jodoin et Hugo Larochelle,<br>
<i>International Conference on Pattern Recognition</i>, 2014<br><br>
<li> <b>A Deep and Tractable Density Estimator [<a href="publications/Uria14.pdf">pdf</a>] </b><br>
Benigno Uria, Iain Murray et Hugo Larochelle,<br>
<i>International Conference on Machine Learning</i>, 2014<br><br>
<li> <b>Agnostic Bayesian Learning of Ensembles [<a href="publications/Lacoste14.pdf">pdf</a>] </b><br>
Alexandre Lacoste, Mario Marchand, François Laviolette et Hugo Larochelle,<br>
<i>International Conference on Machine Learning</i>, 2014<br><br>
<li> <b>RNADE: The real-valued neural autoregressive density-estimator [<a href="publications/Uria2013.pdf">pdf</a>] </b><br>
Benigno Uria, Iain Murray et Hugo Larochelle,<br>
<i>Advances in Neural Information Processing Systems 26</i>, 2013<br><br>
<li> <b>A Neural Autoregressive Topic Model [<a href="publications/nips_2012_camera_ready.pdf">pdf</a>] [<a href="publications/nips_2012_supplementary_camera_ready.pdf">supp</a>] [<a href="http://www.dmi.usherb.ca/~larocheh/code/DocNADE.zip">code</a>]</b><br>
Hugo Larochelle et Stanislas Lauly,<br>
<i>Advances in Neural Information Processing Systems 25</i>, 2012<br><br>
<li> <b>Practical Bayesian Optimization of Machine Learning Algorithms [<a href="publications/gpopt_nips.pdf">pdf</a>] [<a href="publications/gpopt_nips_appendix.pdf">supp</a>] [<a href="http://www.cs.toronto.edu/~jasper/software.html">code</a>]</b><br>
Jasper Snoek, Hugo Larochelle et Ryan P. Adams,<br>
<i>Advances in Neural Information Processing Systems 25</i>, 2012<br><br>
<li> <b> Learning to Rank By Aggregating Expert Preferences [<a href="publications/cikm_2012.pdf" target=_top>pdf</a>]</b><br>
Maksims Volkovs, Hugo Larochelle et Richard Zemel,<br>
<i>International Conference on Information and Knowledge Management</i>, 2012<br><br>
<li> <b> Training Restricted Boltzmann Machines on Word Observations [<a href="publications/wrrbm_icml2012.pdf" target=_top>pdf</a>]</b><br>
George E. Dahl, Ryan P. Adams et Hugo Larochelle,<br>
<i>International Conference on Machine Learning</i>, 2012<br><br>
<li> <b> On Nonparametric Guidance for Learning Autoencoder Representations [<a href="publications/aistats_2012.pdf" target=_top>pdf</a>]</b><br>
Jasper Snoek, Ryan P. Adams et Hugo Larochelle,<br>
<i>Artificial Intelligence and Statistics</i>, 2012<br><br>
<li> <b> Classification of Sets using Restricted Boltzmann Machines [<a href="publications/class_set_rbms_uai.pdf" target=_top>pdf</a>] [<a href="publications/class_set_rbms_additional_material.pdf" target=_top>supp</a>] [<a href="http://arxiv.org/abs/1103.4896" target=_top>arxiv</a>]</b><br>
Jérôme Louradour et Hugo Larochelle,<br>
<i>Uncertainty in Artificial Intelligence</i>, 2011<br><br>
<li> <b> Conditional Restricted Boltzmann Machines for Structured Output Prediction [<a href="publications/struct_output_crbm_uai.pdf" target=_top>pdf</a>]</b><br>
Volodymyr Mnih, Hugo Larochelle et Geoffrey Hinton,<br>
<i>Uncertainty in Artificial Intelligence</i>, 2011<br><br>
<li> <b> Learning Attentional Policies for Tracking and Recognition in Video with Deep Networks [<a href="publications/rbmTracking.pdf" target=_top>pdf</a>] [<a href="http://techtalks.tv/talks/54324/">présentation</a>] [<a href="http://www.youtube.com/watch?v=e14xHDS-Cnk&feature=youtu.be">youtube</a>]</b><br>
Loris Bazzani, Nando de Freitas, Hugo Larochelle, Vittorio Murino et Jo-Anne Ting,<br>
<i> International Conference on Machine Learning proceedings</i>, 2011<br><br>
<li><b> The Neural Autoregressive Distribution Estimator [<a href="publications/aistats2011_nade.pdf" target=_top>pdf</a>] [<a href="http://videolectures.net/aistats2011_larochelle_neural/">présentation</a>] [<a href="code/nade.tar.gz">code</a>] </b> <br>
Hugo Larochelle et Iain Murray,<br>
<i>Artificial Intelligence and Statistics</i>, 2011<br>
<b><i>Notable Paper Award</i></b><br><br>
<li> <b>Learning to combine foveal glimpses with a third-order Boltzmann machine [<a href="publications/nips_eyebm.pdf" target=_top>pdf</a>] [<a href="publications/nips_eyebm_supp.pdf" target=_top>supp</a>] [<a href="http://videolectures.net/nips2010_larochelle_lcf/">présentation</a>] [<a href="http://www.youtube.com/watch?v=Pl6Z-ZAldSY&feature=player_embedded">faces video</a>]</b><br>
Hugo Larochelle et Geoffrey Hinton,<br>
<i>Advances in Neural Information Processing Systems 23</i>, 2010<br><br>
<li><b>Efficient Learning of Deep Boltzmann Machines [<a href="publications/aistats_2010_dbm_recnet.pdf" target=_top>pdf</a>][<a href="code/dbm_recnet.tar.gz" target=_top>code</a>]</b><br>
Ruslan Salakhutdinov et Hugo Larochelle,<br>
<i>Artificial Intelligence and Statistics</i>, 2010<br><br>
<li> <b>Deep Learning using Robust Interdependent Codes [<a href="publications/aistats_2009_robust_interdependent.pdf" target=_top>pdf</a>]</b><br>
Hugo Larochelle, Dumitru Erhan et Pascal Vincent,<br>
<i>Artificial Intelligence and Statistics</i>, 2009<br><br>
<li> <b>Classification using Discriminative Restricted Boltzmann Machines [<a href="publications/icml-2008-discriminative-rbm.pdf" target=_top>pdf</a>] [<a href="http://videolectures.net/icml08_larochelle_cud/">présentation</a>]</b><br>
Hugo Larochelle et Yoshua Bengio,<br>
<i> International Conference on Machine Learning proceedings</i>, 2008<br><br>
<li> <b>Extracting and Composing Robust Features with Denoising Autoencoders
[<a href="publications/icml-2008-denoising-autoencoders.pdf" target=_top>pdf</a>]
</b><br>Pascal Vincent, Hugo Larochelle, Yoshua Bengio et Pierre-Antoine Manzagol,<br>
<i> International Conference on Machine Learning proceedings</i>, 2008<br><br>
<li> <b>Zero-data Learning of New Tasks [<a href="publications/aaai2008_zero-data.pdf" target=_top>pdf</a>]</b><br>
Hugo Larochelle, Dumitru Erhan et Yoshua Bengio,<br>
<i>AAAI Conference on Artificial Intelligence</i>, 2008<br><br>
<li> <b>An Empirical Evaluation of Deep Architectures on Problems with Many Factors of Variation [<a href="publications/deep-nets-icml-07.pdf" target=_top>pdf</a>][<a href="http://www.iro.umontreal.ca/~lisa/twiki/bin/view.cgi/Public/DeepVsShallowComparisonICML2007">html</a>]</b><br>
Hugo Larochelle, Dumitru Erhan, Aaron Courville, James Bergstra et Yoshua Bengio,<br>
<i> International Conference on Machine Learning proceedings</i>, 2007<br><br>
<li> <b>Greedy Layer-Wise Training of Deep Networks [<a href="publications/greedy-deep-nets-nips-06.pdf" target=_top>pdf</a>]</b><br>
Yoshua Bengio, Pascal Lamblin, Dan Popovici et Hugo Larochelle,<br>
<i>Advances in Neural Information Processing Systems 19</i>, 2007<br><br>
<li> <b>Non-Local Manifold Parzen Windows [<a href="publications/nlmp-nips-05.pdf" target=_top>pdf</a>]</b><br>
Yoshua Bengio, Hugo Larochelle et Pascal Vincent,<br>
<i>Advances in Neural Information Processing Systems 18</i>, 2006<br>
</ul>
<h2> Thèse </h2>
<ul>
<li> <b>Études de techniques d'appentissage non-supervisé pour l'amélioration de l'entraînement supervisé de modèles connexionnistes
[<a href="publications/thesis.pdf" target=_top>pdf</a>]</b><br>
Hugo Larochelle,<br>
<i>Thèse de doctorat, Université de Montréal</i>, 2009<br><br>
</ul>
<h2> Articles de colloques et rapports techniques </h2>
<ul>
<li> <b> Correlational Neural Networks [<a href="http://arxiv.org/abs/1504.07225">arxiv</a>] </b><br>
Sarath Chandar, Mitesh M. Khapra, Hugo Larochelle et Balaraman Ravindran,<br>
arXiv, 2015<br><br>
<li> <b> An Infinite Restricted Boltzmann Machine [<a href="http://arxiv.org/abs/1502.02476">arxiv</a>] </b><br>
Marc-Alexandre Côté et Hugo Larochelle,<br>
arXiv, 2015<br><br>
<li> <b> Using Descriptive Video Services to Create a Large Data Source for Video Annotation Research [<a href="http://arxiv.org/abs/1503.01070">arxiv</a>] </b><br>
Atousa Torabi, Christopher Pal, Hugo Larochelle et Aaron Courville,<br>
arXiv, 2015<br><br>
<li> <b> Learning Multilingual Word Representations using a Bag-of-Words Autoencoder [<a href="publications/bilingual_word_rep_worskhop.pdf">pdf</a>] [<a href="http://arxiv.org/abs/1401.1803">arxiv</a>]</b><br>
Stanislas Lauly, Alex Boulanger et Hugo Larochelle,<br>
NIPS Deep Learning Workshop, 2013<br>
<br>
<li> <b> Loss-sensitive Training of Probabilistic Conditional Random Fields [<a href="http://arxiv.org/abs/1107.1805" target=_top>arxiv</a>]</b><br>
Maksim Volkovs, Hugo Larochelle et Richard Zemel,<br>
arXiv, 2011<br>
<br>
<li> <b> Autotagging music with conditional restricted Boltzmann machines [<a href="http://arxiv.org/abs/1103.2832" target=_top>arxiv</a>]</b><br>
Michael Mandel, Razvan Pascanu, Hugo Larochelle et Yoshua Bengio,<br>
arXiv, 2011<br>
<br>
<li> <b>Extracting and Composing Robust Features with Denoising Autoencoders
[<a href="publications/denoising_autoencoders_tr1316.pdf" target=_top>pdf</a>]
</b><br>Pascal Vincent, Hugo Larochelle, Yoshua Bengio et Pierre-Antoine Manzagol,<br>
Rapport Technique #1316, Département d'informatique et recherche opérationnelle,<br>
Université de Montréal, 2008<br>
<br>
<li> <b>Distributed Representation Prediction for Generalization to New Words
[<a href="publications/dist_rep_pred_tr1284.pdf" target=_top>pdf</a>]
</b><br>Hugo Larochelle et Yoshua Bengio,<br>
Rapport Technique #1284, Département d'informatique et recherche opérationnelle,<br>
Université de Montréal, 2006<br>
<br>
<li> <b>Greedy Layer-Wise Training of Deep Networks
[<a href="publications/dbn_supervised_tr1282.pdf" target=_top>pdf</a>]
</b><br>Yoshua Bengio, Pascal Lamblin, Dan Popovici et Hugo Larochelle,<br>
Rapport Technique #1282, Département d'informatique et recherche opérationnelle,<br>
Université de Montréal, 2006<br>
<br>
<li> <b>Non-Local Manifold Parzen Windows
[<a href="publications/nlmp-techreport.pdf" target=_top>pdf</a>]
[<a href="publications/nlmp-techreport.ps" target=_top>ps</a>]
</b><br>Yoshua Bengio et Hugo Larochelle,<br>
Rapport Technique #1264, Département d'informatique et recherche opérationnelle,<br>
Université de Montréal, 2005<br>
</ul>
<h2> Autres </h2>
<ul>
<li> <b>Classification using Discriminative Restricted Boltzmann Machines
[<a href="publications/drbm-mitacs-poster.pdf" target=_top>pdf</a>]
</b><br>
Hugo Larochelle et Yoshua Bengio,<br>
Deuxième Congrès Canada-France MITACS,<br>
<b><i>Troisième place à la compétition d'affiche</i></b>,<br>
Montréal, Canada, 2008<br><br>
<li> <b>Deep Woods
[<a href="publications/deep-woods-snowbird.pdf" target=_top>pdf</a>]
</b><br>
Yoshua Bengio, Hugo Larochelle et Joseph Turian,<br>
Affiche présentée au <i>Learning@Snowbird Workshop</i>, <br>
Snowbird, USA, 2008<br><br>
<li> <b>Generalization to a zero-data task: an empirical study [<a href="publications/snowbird-poster-slides-2007.pdf" target=_top>pdf</a>] [<a href="publications/snowbird-poster-slides-2007.ps" target=_top>ps</a>]</b>
<br>
Hugo Larochelle, Dumitru Erhan et Yoshua Bengio<br>
Présentation et affiche présentées au <i>Learning Workshop</i>,<br>
San Juan, Puerto Rico, 2007<br><br>
<li> <b>Didactiel sur les réseaux de neurones en traitement
de la langue [<a href="publications/nnet-nlp-rali.pdf" target=_top>pdf</a>]</b>
<br>
Hugo Larochelle,<br>
Présentation dans le cadre des séminaires RALI-OLST,<br>
Université de Montréal, 2006<br><br>
<li><b>Non-Local Manifold Parzen Windows [<a href="publications/nlmp-CIAR-slides.pdf" target=_top>pdf</a>]</b><br>
Yoshua Bengio, Hugo Larochelle et Pascal Vincent,<br>
Présentation à la <i>CIAR Summer School</i>,<br>
University of Toronto, 2005<br><br>
<li> <b>Non-Local Manifold Parzen Windows
[<a href="publications/snowbird-poster.pdf" target=_top>pdf</a>]
[<a href="publications/snowbird-poster.ps" target=_top>ps</a>]
</b><br>
Yoshua Bengio et Hugo Larochelle,<br>
Affiche présentée au <i>Learning@Snowbird Workshop</i>, <br>
Snowbird, USA, 2005<br><br>
<li> <b>Implantation et analyse dun modèle graphique de
désambiguïsation<br> à entraînement supervisé, semi-supervisé
et non-supervisé
[<a href="ift3051/rapport_final.pdf" target=_top>pdf</a>]
[<a href="ift3051/rapport_final.ps" target=_top>ps</a>]<!--
[<a href="ift3051" target=_top>site</a>]--></b><br>
Hugo Larochelle et Yoshua Bengio,<br>
Projet IFT3051, Département d'informatique et recherche opérationnelle,<br>
Université de Montréal, 2004.<br>
<br>
<li> <b>Some Supervised Models in Disambiguation
[<a href="publications/hugo_mitacs_disambiguation.ppt" target=_top>ppt</a>]</b><br>
Hugo Larochelle, Christian Jauvin et Yoshua Bengio,<br>
Affiche présentée à la conférence Échanges Québec de MITACS,<br>
Montréal, Canada, 2003.<br>
<br>
<li> <b>Étude de la pertinence de métriques statistiques
pour la détection de termes dans un document
[<a href="publications/memoire.pdf" target=_top>pdf</a>]
[<a href="publications/memoire.ps" target=_top>ps</a>]
</b><br>
Hugo Larochelle et Philippe Langlais,<br>
Rapport de stage CRSNG au laboratoire RALI,<br>
Département d'informatique et recherche opérationnelle,<br>
Université de Montréal, été 2002.<br>
</ul>
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