Journal papers

• Deep learning with coherent nanophotonic circuits [link]
  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,
  Nature Photonics, 2017
  
  
• Movie Description [link]
  Anna Rohrbach, Atousa Torabi, Marcus Rohrbach, Niket Tandon, Christopher Pal, Hugo Larochelle, Aaron Courville and Bernt Schiele,
  International Journal of Computer Vision, 1-27, 2017
  
  
• Traffic Analytics with Low Frame Rate Videos [link]
  Zhiming Luo, Pierre-Marc Jodoin, Song-Zhi Su, Shao-Zi Li and Hugo Larochelle,
  IEEE Transactions on Circuits and Systems for Video Technology, 2016
  
  
• Neural Autoregressive Distribution Estimation [pdf]
  Benigno Uria, Marc-Alexandre Côté, Karol Gregor, Iain Murray and Hugo Larochelle,
  Journal of Machine Learning Research, 17(205): 1-37, 2016
  
  
• An Infinite Restricted Boltzmann Machine [pdf] [arxiv]
  Marc-Alexandre Côté and Hugo Larochelle,
  Neural Computation, 28(7): 1265-1288, 2016
  
  
• Domain-Adversarial Training of Neural Networks [pdf]
  Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand and Victor Lempitsky,
  Journal of Machine Learning Research, 17(59): 1-35, 2016
  
  
• Correlational Neural Networks [pdf] [arxiv]
  Sarath Chandar, Mitesh M. Khapra, Hugo Larochelle and Balaraman Ravindran,
  Neural Computation, 28(2): 286-304, 2016
  
  
• A Deep and Autoregressive Approach for Topic Modeling of Multimodal Data [arxiv]
  Yin Zheng, Yu-Jin Zhang and Hugo Larochelle,
  IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(6): 1056-1069, 2016
  
  
• Within-Brain Classification for Brain Tumor Segmentation [arxiv]
  Mohammad Havaei, Hugo Larochelle, Philippe Poulin and Pierre-Marc Jodoin,
  International Journal of Computer Assisted Radiology and Surgery, 1-12, 2015
  
  
• A Neural Autoregressive Approach to Attention-based Recognition [pdf]
  Yin Zheng, Richard Zemel, Yu-Jin Zhang and Hugo Larochelle,
  International Journal of Computer Vision, 113(1): 67-79, 2015
  
  
• PhenoVar: a phenotype-driven approach in clinical genomics for the diagnosis of polymalformative syndromes [html]
  Yannis J Trakadis, Caroline Buote, Jean-François Therriault, Pierre-Étienne Jacques, Hugo Larochelle and Sébastien Lévesque,
  BMC Medical Genomics, 7(22), 2014
  
  
• Nonparametric Guidance of Autoencoder Representations using Label Information [pdf]
  Jasper Snoek, Ryan P. Adams and Hugo Larochelle,
  Journal of Machine Learning Research, 13(Sep): 2567-2588, 2012
  
  
• Learning Where to Attend With Deep Architectures for Image Tracking [pdf] [html]
  Misha Denil, Loris Bazzani, Hugo Larochelle and Nando de Freitas,
  Neural Computation, 24(8): 2151-2184, 2012
  
  
• Learning Algorithms for the Classification Restricted Boltzmann Machine [pdf]
  Hugo Larochelle, Michael Mandel, Razvan Pascanu and Yoshua Bengio,
  Journal of Machine Learning Research, 13(Mar): 643-669, 2012
  
  
• Detonation Classification from Acoustic Signature with the Restricted Boltzmann Machine [pdf]
  Yoshua Bengio, Nicolas Chapados, Olivier Delalleau, Hugo Larochelle, Xavier Saint-Mleux, Christian Hudon and Jérôme Louradour,
  Computational Intelligence, 28(2): 261-288, 2012
  
  
• Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion [pdf]
  Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio and Pierre-Antoine Manzagol,
  Journal of Machine Learning Research, 11(Dec): 3371-3408, 2010
  
  
• Tractable Multivariate Binary Density Estimation and the Restricted Boltzmann Forest [pdf]
  Hugo Larochelle, Yoshua Bengio and Joseph Turian,
  Neural Computation, 22(9): 2285-2307, 2010
  
  
• Exploring Strategies for Training Deep Neural Networks [pdf]
  Hugo Larochelle, Yoshua Bengio, Jérôme Louradour and Pascal Lamblin,
  Journal of Machine Learning Research, 10(Jan): 1-40, 2009
  
  
• Non-Local Estimation of Manifold Structure [pdf] [ps]
  Yoshua Bengio, Martin Monperrus and Hugo Larochelle,
  Neural Computation, 18(10): 2509-2528, 2006
  

Conference Papers

• GuessWhat?! Visual object discovery through multi-modal dialogue [pdf] [html]
  Harm de Vries, Florian Strub, Sarath Chandar, Olivier Pietquin, Hugo Larochelle and Aaron Courville,
  IEEE Conference on Computer Vision and Pattern Recognition, 2017
  
  
• Optimization as a Model for Few-Shot Learning [pdf]
  Sachin Ravi and Hugo Larochelle,
  International Conference on Learning Representations, 2017
  
  
• Recurrent Mixture Density Network for Spatiotemporal Visual Attention [pdf]
  Loris Bazzani, Hugo Larochelle and Lorenzo Torresani,
  International Conference on Learning Representations, 2017
  
  
• Autoencoding beyond pixels using a learned similarity metric [pdf]
  Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle and Ole Winther,
  International Conference on Machine Learning, 2016
  
  
• Dynamic Capacity Networks [pdf]
  Amjad Almahairi, Nicolas Ballas, Tim Cooijmans, Yin Zheng, Hugo Larochelle and Aaron Courville,
  International Conference on Machine Learning, 2016
  
  
• Describing Videos by Exploiting Temporal Structure [arxiv]
  Li Yao, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher Pal, Hugo Larochelle and Aaron Courville,
  International Conference on Computer Vision, 2015
  
  
• MADE: Masked Autoencoder for Distribution Estimation [pdf] [supp]
  Mathieu Germain, Karol Gregor, Iain Murray and Hugo Larochelle,
  International Conference on Machine Learning, 2015
  
  
• Using a Recursive Neural Network to Learn an Agent's Decision Model for Plan Recognition [pdf]
  Francis Bisson, Hugo Larochelle and Froduald Kabanza,
  International Joint Conference on Artificial Intelligence, 2015
  
  
• An Autoencoder Approach to Learning Bilingual Word Representations [pdf]
  Sarath Chandar, Stanislas Lauly, Hugo Larochelle, Mitesh M. Khapra, Balaraman Ravindran, Vikas Raykar et Amrita Saha,
  Advances in Neural Information Processing Systems 27, 2014
  
  
• Sequential Model-Based Ensemble Optimization [pdf]
  Alexandre Lacoste, Hugo Larochelle, Mario Marchand and François Laviolette,
  Uncertainty in Artificial Intelligence, 2014
  
  
• Leveraging user libraries to bootstrap collaborative filtering [pdf]
  Laurent Charlin, Richard Zemel and Hugo Larochelle,
  ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2014
  
  
• Topic Modeling of Multimodal Data: an Autoregressive Approach [pdf]
  Yin Zheng, Yu-Jin Zhang and Hugo Larochelle,
  IEEE Conference on Computer Vision and Pattern Recognition, 2014
  
  
• Efficient interactive brain tumor segmentation as within-brain kNN classification [pdf]
  Mohammad Havaei, Pierre-Marc Jodoin and Hugo Larochelle,
  International Conference on Pattern Recognition, 2014
  
  
• A Deep and Tractable Density Estimator [pdf]
  Benigno Uria, Iain Murray and Hugo Larochelle,
  International Conference on Machine Learning, 2014
  
  
• Agnostic Bayesian Learning of Ensembles [pdf]
  Alexandre Lacoste, Mario Marchand, François Laviolette and Hugo Larochelle,
  International Conference on Machine Learning, 2014
  
  
• RNADE: The real-valued neural autoregressive density-estimator [pdf]
  Benigno Uria, Iain Murray and Hugo Larochelle,
  Advances in Neural Information Processing Systems 26, 2013
  
  
• A Neural Autoregressive Topic Model [pdf] [supp] [code]
  Hugo Larochelle and Stanislas Lauly,
  Advances in Neural Information Processing Systems 25, 2012
  
  
• Practical Bayesian Optimization of Machine Learning Algorithms [pdf] [supp] [code]
  Jasper Snoek, Hugo Larochelle and Ryan P. Adams,
  Advances in Neural Information Processing Systems 25, 2012
  
  
• Learning to Rank By Aggregating Expert Preferences [pdf]
  Maksims Volkovs, Hugo Larochelle and Richard Zemel,
  International Conference on Information and Knowledge Management, 2012
  
  
• Training Restricted Boltzmann Machines on Word Observations [pdf]
  George E. Dahl, Ryan P. Adams and Hugo Larochelle,
  International Conference on Machine Learning, 2012
  
  
• On Nonparametric Guidance for Learning Autoencoder Representations [pdf]
  Jasper Snoek, Ryan P. Adams and Hugo Larochelle,
  Artificial Intelligence and Statistics, 2012
  
  
• Classification of Sets using Restricted Boltzmann Machines [pdf] [supp] [arxiv]
  Jérôme Louradour and Hugo Larochelle,
  Uncertainty in Artificial Intelligence, 2011
  
  
• Conditional Restricted Boltzmann Machines for Structured Output Prediction [pdf]
  Volodymyr Mnih, Hugo Larochelle and Geoffrey Hinton,
  Uncertainty in Artificial Intelligence, 2011
  
  
• Learning Attentional Policies for Tracking and Recognition in Video with Deep Networks [pdf] [talk] [youtube]
  Loris Bazzani, Nando de Freitas, Hugo Larochelle, Vittorio Murino and Jo-Anne Ting,
  International Conference on Machine Learning, 2011
  
  
• The Neural Autoregressive Distribution Estimator [pdf] [talk] [code]
  Hugo Larochelle and Iain Murray,
  Artificial Intelligence and Statistics, 2011
  Notable Paper Award
  
  
• Learning to combine foveal glimpses with a third-order Boltzmann machine [pdf] [supp] [talk] [faces video]
  Hugo Larochelle and Geoffrey Hinton,
  Advances in Neural Information Processing Systems 23, 2010
  
  
• Efficient Learning of Deep Boltzmann Machines [pdf][code]
  Ruslan Salakhutdinov and Hugo Larochelle,
  Artificial Intelligence and Statistics, 2010
  
  
• Deep Learning using Robust Interdependent Codes [pdf]
  Hugo Larochelle, Dumitru Erhan and Pascal Vincent,
  Artificial Intelligence and Statistics, 2009
  
  
• Classification using Discriminative Restricted Boltzmann Machines [pdf] [talk]
  Hugo Larochelle and Yoshua Bengio,
  International Conference on Machine Learning, 2008
  
  
• Extracting and Composing Robust Features with Denoising Autoencoders [pdf]
  Pascal Vincent, Hugo Larochelle, Yoshua Bengio and Pierre-Antoine Manzagol,
  International Conference on Machine Learning, 2008
  
  
• Zero-data Learning of New Tasks [pdf]
  Hugo Larochelle, Dumitru Erhan and Yoshua Bengio,
  AAAI Conference on Artificial Intelligence, 2008
  
  
• An Empirical Evaluation of Deep Architectures on Problems with Many Factors of Variation [pdf][html]
  Hugo Larochelle, Dumitru Erhan, Aaron Courville, James Bergstra and Yoshua Bengio,
  International Conference on Machine Learning, 2007
  
  
• Greedy Layer-Wise Training of Deep Networks [pdf]
  Yoshua Bengio, Pascal Lamblin, Dan Popovici and Hugo Larochelle,
  Advances in Neural Information Processing Systems 19, 2007
  
  
• Non-Local Manifold Parzen Windows [pdf]
  Yoshua Bengio, Hugo Larochelle and Pascal Vincent,
  Advances in Neural Information Processing Systems 18, 2006
  

Thesis

• Études de techniques d'appentissage non-supervisé pour l'amélioration de l'entraînement supervisé de modèles connexionnistes [pdf]
  Hugo Larochelle,
  Ph.D. thesis, Université de Montréal, 2009
  
  

Workshop Papers and Technical Reports

• An Infinite Restricted Boltzmann Machine [arxiv]
  Marc-Alexandre Côté and Hugo Larochelle,
  arXiv, 2015
  
  
• Using Descriptive Video Services to Create a Large Data Source for Video Annotation Research [arxiv]
  Atousa Torabi, Christopher Pal, Hugo Larochelle and Aaron Courville,
  arXiv, 2015
  
  
• Learning Multilingual Word Representations using a Bag-of-Words Autoencoder [pdf] [arxiv]
  Stanislas Lauly, Alex Boulanger and Hugo Larochelle,
  NIPS Deep Learning Workshop, 2013
  
  
• Loss-sensitive Training of Probabilistic Conditional Random Fields [arxiv]
  Maksims Volkovs, Hugo Larochelle and Richard Zemel,
  arXiv, 2011
  
  
• Autotagging music with conditional restricted Boltzmann machines [arxiv]
  Michael Mandel, Razvan Pascanu, Hugo Larochelle and Yoshua Bengio,
  arXiv, 2011
  
  
• Extracting and Composing Robust Features with Denoising Autoencoders [pdf]
  Pascal Vincent, Hugo Larochelle, Yoshua Bengio and Pierre-Antoine Manzagol,
  Technical Report #1316, Département d'informatique et recherche opérationnelle,
  Université de Montréal, 2008
  
  
• Distributed Representation Prediction for Generalization to New Words [pdf]
  Hugo Larochelle and Yoshua Bengio,
  Technical Report #1284, Département d'informatique et recherche opérationnelle,
  Université de Montréal, 2006
  
  
• Greedy Layer-Wise Training of Deep Networks [pdf]
  Yoshua Bengio, Pascal Lamblin, Dan Popovici and Hugo Larochelle,
  Technical Report #1282, Département d'informatique et recherche opérationnelle,
  Université de Montréal, 2006
  
  
• Non-Local Manifold Parzen Windows [pdf] [ps]
  Yoshua Bengio and Hugo Larochelle,
  Technical Report #1264, Département d'informatique et recherche opérationnelle,
  Université de Montréal, 2005
  

 Others

• Classification using Discriminative Restricted Boltzmann Machines [pdf]
  Hugo Larochelle and Yoshua Bengio,
  MITACS Second Canada-France Congress,
  Third place at poster competition,
  Montreal, Canada, 2008
  
  
• Deep Woods [pdf]
  Yoshua Bengio, Hugo Larochelle and Joseph Turian,
  Poster presented at the Learning@Snowbird Workshop,
  Snowbird, USA, 2008
  
  
• Generalization to a zero-data task: an empirical study [pdf] [ps]
  Hugo Larochelle, Dumitru Erhan and Yoshua Bengio
  Talk and poster presented at the Learning Workshop,
  San Juan, Puerto Rico, 2007
  
  
• Didactiel sur les réseaux de neurones en traitement de la langue [pdf]
  Hugo Larochelle,
  Talk in the RALI-OLST seminar series
  Université de Montréal, 2006
  
  
• Non-Local Manifold Parzen Windows [pdf]
  Yoshua Bengio, Hugo Larochelle and Pascal Vincent,
  Talk at the CIAR Summer School,
  University of Toronto, 2005
  
  
• Non-Local Manifold Parzen Windows [pdf] [ps]
  Yoshua Bengio and Hugo Larochelle,
  Poster presented at the Learning@Snowbird Workshop,
  Snowbird, USA, 2005
  
  
• Implantation et analyse d’un modèle graphique de désambiguïsation
  à entraînement supervisé, semi-supervisé et non-supervisé [pdf] [ps]
  Hugo Larochelle and Yoshua Bengio,
  IFT3051 project, Département d'informatique et recherche opérationnelle,
  Université de Montréal, 2004.
  
  
• Some Supervised Models in Disambiguation [ppt]
  Hugo Larochelle, Christian Jauvin and Yoshua Bengio,
  Poster presented at MITACS Quebec Interchange,
  Montréal, Canada, 2003.
  
  
• Étude de la pertinence de métriques statistiques pour la détection de termes dans un document [pdf] [ps]
  Hugo Larochelle and Philippe Langlais,
  NSERC Internship report at RALI lab,
  Département d'informatique et recherche opérationnelle,
  Université de Montréal, été 2002.