Journal papers
- Document Neural Autoregressive Distribution Estimation [pdf]
Stanislas Lauly, Yin Zheng, Alexandre Allauzen and Hugo Larochelle,
Journal of Machine Learning Research, 18(113): 1-24, 2017
- 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
- Brain tumor segmentation with deep neural networks [link] [arXiv]
Mohammad Havaei, Axel Davy, David Warde-Farley, Antoine Biard, Aaron Courville, Yoshua Bengio, Chris Pal, Pierre-Marc Jodoin and Hugo Larochelle,
Medical Image Analysis, 35: 18-31, 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
- Transfer and Exploration via the Information Bottleneck [pdf]
Anirudh Goyal, Riashat Islam, DJ Strouse, Zafarali Ahmed, Hugo Larochelle, Matthew Botvinick, Sergey Levine and Yoshua Bengio,
International Conference on Learning Representations, 2019
- Recall Traces: Backtracking Models for Efficient Reinforcement Learning [pdf]
Anirudh Goyal, Philemon Brakel, William Fedus, Soumye Singhal, Timothy Lillicrap, Sergey Levine, Hugo Larochelle and Yoshua Bengio,
International Conference on Learning Representations, 2019
- Meta-learning for semi-supervised few-shot classification [pdf]
Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua Tenenbaum, Hugo Larochelle and Richard Zemel,
International Conference on Learning Representations, 2018
- A Meta-Learning Perspective on Cold-Start Recommendations for Items [pdf]
Manasi Vartak, Arvind Thiagarajan, Conrado Miranda, Jeshua Bratman and Hugo Larochelle,
Advances in Neural Information Processing Systems 30, 2017
- Modulating early visual processing by language [pdf]
Harm de Vries, Florian Strub, Jérémie Mary, Hugo Larochelle, Olivier Pietquin and Aaron Courville,
Advances in Neural Information Processing Systems 30, 2017
- Learn to Track: Deep Learning for Tractography [pdf]
Philippe Poulin, Marc-Alexandre Cote, Jean-Christophe Houde, Laurent Petit, Peter Florian Neher, Klaus H. Maier-Hein, Hugo Larochelle and Maxime Descoteaux,
Medical Image Computing and Computer Assisted Intervention, 2017
- 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.