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<!DOCTYPE html >
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<title>Hugo Larochelle</title>
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Hugo Larochelle
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<li><a href="index_en.html" accesskey="H"><em>H</em>ome</a></li>
<li><a href="publications_en.html" accesskey="p" class="active"><em>P</em>ublications</a></li>
<li><a href="university_en.html" accesskey="u"><em>U</em>niversity</a></li>
<li><a href="links_en.html" accesskey="l"><em>L</em>inks</a></li>
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<div id="contentnobars">
<h2>Journal papers</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 and 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 and 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 and 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">html</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 and Marin Soljacic,<br>
<i>Nature Photonics</i>, 2017<br><br>
<li> <b> Movie Description [<a href="http://rdcu.be/oN9P">html</a>] </b><br>
Anna Rohrbach, Atousa Torabi, Marcus Rohrbach, Niket Tandon, Christopher Pal, Hugo Larochelle, Aaron Courville and 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://arxiv.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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and Hugo Larochelle,<br>
<i>Neural Computation</i>, 18(10): 2509-2528, 2006<br>
</ul>
<h2> Conference Papers </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>] [<a href="https://github.com/google-research/meta-dataset">github</a>] </b><br>
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and Aaron Courville,<br>
<i>International Conference on Computer Vision</i>, 2015<br><br>
<li> <b> MADE: Masked Autoencoder for Distribution Estimation [<a href="http://jmlr.org/proceedings/papers/v37/germain15.pdf">pdf</a>] [<a href="http://jmorg/proceedings/papers/v37/germain15-supp.pdf">supp</a>] </b><br>
Mathieu Germain, Karol Gregor, Iain Murray and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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 and 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/">talk</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 and Jo-Anne Ting,<br>
<i> International Conference on Machine Learning</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/">talk</a>] [<a href="code/nade.tar.gz">code</a>] </b> <br>
Hugo Larochelle and 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/">talk</a>] [<a href="http://www.youtube.com/watch?v=Pl6Z-ZAldSY&feature=player_embedded">faces video</a>]</b><br>
Hugo Larochelle and 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 and 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 and 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/">talk</a>]</b><br>
Hugo Larochelle and Yoshua Bengio,<br>
<i> International Conference on Machine Learning</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 and Pierre-Antoine Manzagol,<br>
<i> International Conference on Machine Learning</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 and 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 and Yoshua Bengio,<br>
<i> International Conference on Machine Learning</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 and 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 and Pascal Vincent,<br>
<i>Advances in Neural Information Processing Systems 18</i>, 2006<br>
</ul>
<h2> Thesis </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>Ph.D. thesis, Université de Montréal</i>, 2009<br><br>
</ul>
<h2> Workshop Papers and Technical Reports </h2>
<ul>
<li> <b> An Infinite Restricted Boltzmann Machine [<a href="http://arxiv.org/abs/1502.02476">arxiv</a>] </b><br>
Marc-Alexandre Côté and 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 and 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 and 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>
Maksims Volkovs, Hugo Larochelle and 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 and 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 and Pierre-Antoine Manzagol,<br>
Technical Report #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 and Yoshua Bengio,<br>
Technical Report #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 and Hugo Larochelle,<br>
Technical Report #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 and Hugo Larochelle,<br>
Technical Report #1264, Département d'informatique et recherche opérationnelle,<br>
Université de Montréal, 2005<br>
</ul>
<h2> Others </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 and Yoshua Bengio,<br>
MITACS Second Canada-France Congress,<br>
<b><i>Third place at poster competition</i></b>,<br>
Montreal, 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 and Joseph Turian,<br>
Poster presented at the <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 and Yoshua Bengio<br>
Talk and poster presented at the <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>
Talk in the RALI-OLST seminar series<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 and Pascal Vincent,<br>
Talk at the <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 and Hugo Larochelle,<br>
Poster presented at the <i>Learning@Snowbird Workshop</i>,<br>
Snowbird, USA, 2005<br><br>
<li> <b>Implantation et analyse d’un 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 and Yoshua Bengio,<br>
IFT3051 project, 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 and Yoshua Bengio,<br>
Poster presented at MITACS Quebec Interchange,<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 and Philippe Langlais,<br>
NSERC Internship report at RALI lab,<br>
Département d'informatique et recherche opérationnelle,<br>
Université de Montréal, été 2002.<br>
</ul>
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