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- affiliation: Google, USA
- affiliation: Université de Sherbrooke, Department of Computer Science
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Journal Articles
- 2021
[j24]Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Larivière, Alina Beygelzimer, Florence d'Alché-Buc, Emily B. Fox, Hugo Larochelle:
Improving Reproducibility in Machine Learning Research(A Report from the NeurIPS 2019 Reproducibility Program). J. Mach. Learn. Res. 22: 164:1-164:20 (2021)- 2020
- [j23]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, Michael Bowling:
The Hanabi challenge: A new frontier for AI research. Artif. Intell. 280: 103216 (2020) - 2019
- [j22]Iyad Rahwan, Manuel Cebrián
, Nick Obradovich, Josh C. 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, Michael P. Wellman:
Machine behaviour. Nat. 568(7753): 477-486 (2019) - 2018
- [j21]Zhiming Luo, Pierre-Marc Jodoin, Song-Zhi Su, Shao-Zi Li, Hugo Larochelle:
Traffic Analytics With Low-Frame-Rate Videos. IEEE Trans. Circuits Syst. Video Technol. 28(4): 878-891 (2018) - 2017
- [j20]Anna Rohrbach, Atousa Torabi, Marcus Rohrbach, Niket Tandon, Christopher Joseph Pal, Hugo Larochelle, Aaron C. Courville, Bernt Schiele:
Movie Description. Int. J. Comput. Vis. 123(1): 94-120 (2017) - [j19]Stanislas Lauly, Yin Zheng, Alexandre Allauzen, Hugo Larochelle:
Document Neural Autoregressive Distribution Estimation. J. Mach. Learn. Res. 18: 113:1-113:24 (2017) - [j18]Mohammad Havaei, Axel Davy, David Warde-Farley, Antoine Biard, Aaron C. Courville, Yoshua Bengio, Chris Pal, Pierre-Marc Jodoin, Hugo Larochelle:
Brain tumor segmentation with Deep Neural Networks. Medical Image Anal. 35: 18-31 (2017) - 2016
- [j17]Mohammad Havaei, Hugo Larochelle, Philippe Poulin, Pierre-Marc Jodoin:
Within-brain classification for brain tumor segmentation. Int. J. Comput. Assist. Radiol. Surg. 11(5): 777-788 (2016) - [j16]Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor S. Lempitsky:
Domain-Adversarial Training of Neural Networks. J. Mach. Learn. Res. 17: 59:1-59:35 (2016) - [j15]Benigno Uria, Marc-Alexandre Côté, Karol Gregor, Iain Murray, Hugo Larochelle:
Neural Autoregressive Distribution Estimation. J. Mach. Learn. Res. 17: 205:1-205:37 (2016) - [j14]Sarath Chandar, Mitesh M. Khapra, Hugo Larochelle, Balaraman Ravindran:
Correlational Neural Networks. Neural Comput. 28(2): 257-285 (2016) - [j13]Marc-Alexandre Côté, Hugo Larochelle:
An Infinite Restricted Boltzmann Machine. Neural Comput. 28(7): 1265-1288 (2016) - [j12]Yin Zheng, Yu-Jin Zhang, Hugo Larochelle:
A Deep and Autoregressive Approach for Topic Modeling of Multimodal Data. IEEE Trans. Pattern Anal. Mach. Intell. 38(6): 1056-1069 (2016) - 2015
- [j11]Yin Zheng, Richard S. Zemel, Yu-Jin Zhang, Hugo Larochelle:
A Neural Autoregressive Approach to Attention-based Recognition. Int. J. Comput. Vis. 113(1): 67-79 (2015) - [j10]Guo-Jun Qi, Hugo Larochelle, Benoit Huet, Jiebo Luo, Kai Yu:
Guest Editorial: Deep Learning for Multimedia Computing. IEEE Trans. Multim. 17(11): 1873-1874 (2015) - 2013
- [j9]Samy Bengio, Li Deng, Hugo Larochelle, Honglak Lee, Ruslan Salakhutdinov:
Guest Editors' Introduction: Special Section on Learning Deep Architectures. IEEE Trans. Pattern Anal. Mach. Intell. 35(8): 1795-1797 (2013) - 2012
- [j8]Yoshua Bengio, Nicolas Chapados, Olivier Delalleau, Hugo Larochelle, Xavier Saint-Mleux, Christian Hudon, Jérôme Louradour:
Detonation Classification from acoustic Signature with the Restricted Boltzmann Machine. Comput. Intell. 28(2): 261-288 (2012) - [j7]Hugo Larochelle, Michael I. Mandel, Razvan Pascanu, Yoshua Bengio:
Learning Algorithms for the Classification Restricted Boltzmann Machine. J. Mach. Learn. Res. 13: 643-669 (2012) - [j6]Jasper Snoek, Ryan P. Adams, Hugo Larochelle:
Nonparametric guidance of autoencoder representations using label information. J. Mach. Learn. Res. 13: 2567-2588 (2012) - [j5]Misha Denil, Loris Bazzani, Hugo Larochelle, Nando de Freitas:
Learning Where to Attend with Deep Architectures for Image Tracking. Neural Comput. 24(8): 2151-2184 (2012) - 2010
- [j4]Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, Pierre-Antoine Manzagol:
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion. J. Mach. Learn. Res. 11: 3371-3408 (2010) - [j3]Hugo Larochelle, Yoshua Bengio, Joseph P. Turian:
Tractable Multivariate Binary Density Estimation and the Restricted Boltzmann Forest. Neural Comput. 22(9): 2285-2307 (2010) - 2009
- [j2]Hugo Larochelle, Yoshua Bengio, Jérôme Louradour, Pascal Lamblin:
Exploring Strategies for Training Deep Neural Networks. J. Mach. Learn. Res. 10: 1-40 (2009) - 2006
- [j1]Yoshua Bengio, Martin Monperrus, Hugo Larochelle:
Nonlocal Estimation of Manifold Structure. Neural Comput. 18(10): 2509-2528 (2006)
Conference and Workshop Papers
- 2024
- [c68]Dinesh Daultani, Hugo Larochelle:
Consolidating Separate Degradations Model via Weights Fusion and Distillation. WACV (Workshops) 2024: 440-449 - 2023
- [c67]David Bieber, Rishab Goel, Daniel Zheng, Hugo Larochelle, Daniel Tarlow:
Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions. ICLR 2023 - [c66]Disha Shrivastava, Hugo Larochelle, Daniel Tarlow:
Repository-Level Prompt Generation for Large Language Models of Code. ICML 2023: 31693-31715 - [c65]Mélisande Teng, Amna Elmustafa, Benjamin Akera, Yoshua Bengio, Hager Radi Abdelwahed, Hugo Larochelle, David Rolnick:
SatBird: a Dataset for Bird Species Distribution Modeling using Remote Sensing and Citizen Science Data. NeurIPS 2023 - 2022
- [c64]Samarth Sinha, Karsten Roth, Anirudh Goyal, Marzyeh Ghassemi, Zeynep Akata, Hugo Larochelle, Animesh Garg:
Uniform Priors for Data-Efficient Learning. CVPR Workshops 2022: 4016-4027 - [c63]Arman Afrasiyabi, Hugo Larochelle, Jean-François Lalonde, Christian Gagné:
Matching Feature Sets for Few-Shot Image Classification. CVPR 2022: 9004-9014 - [c62]Hattie Zhou, Ankit Vani, Hugo Larochelle, Aaron C. Courville:
Fortuitous Forgetting in Connectionist Networks. ICLR 2022 - [c61]Utku Evci, Vincent Dumoulin, Hugo Larochelle, Michael C. Mozer:
Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning. ICML 2022: 6009-6033 - 2021
- [c60]Samarth Sinha, Homanga Bharadhwaj, Anirudh Goyal, Hugo Larochelle, Animesh Garg, Florian Shkurti:
DIBS: Diversity Inducing Information Bottleneck in Model Ensembles. AAAI 2021: 9666-9674 - [c59]Cristina Nader Vasconcelos, Hugo Larochelle, Vincent Dumoulin, Rob Romijnders, Nicolas Le Roux, Ross Goroshin:
Impact of Aliasing on Generalization in Deep Convolutional Networks. ICCV 2021: 10509-10518 - [c58]Lu Liu, William L. Hamilton, Guodong Long, Jing Jiang, Hugo Larochelle:
A Universal Representation Transformer Layer for Few-Shot Image Classification. ICLR 2021 - [c57]Eleni Triantafillou, Hugo Larochelle, Richard S. Zemel, Vincent Dumoulin:
Learning a Universal Template for Few-shot Dataset Generalization. ICML 2021: 10424-10433 - [c56]Vincent Dumoulin, Neil Houlsby, Utku Evci, Xiaohua Zhai, Ross Goroshin, Sylvain Gelly, Hugo Larochelle:
A Unified Few-Shot Classification Benchmark to Compare Transfer and Meta Learning Approaches. NeurIPS Datasets and Benchmarks 2021 - [c55]Disha Shrivastava, Hugo Larochelle, Daniel Tarlow:
Learning to Combine Per-Example Solutions for Neural Program Synthesis. NeurIPS 2021: 6102-6114 - 2020
- [c54]Vishal Jain, William Fedus, Hugo Larochelle, Doina Precup, Marc G. Bellemare:
Algorithmic Improvements for Deep Reinforcement Learning Applied to Interactive Fiction. AAAI 2020: 4328-4336 - [c53]Massimo Caccia, Lucas Caccia, William Fedus, Hugo Larochelle, Joelle Pineau, Laurent Charlin:
Language GANs Falling Short. ICLR 2020 - [c52]Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, Hugo Larochelle:
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples. ICLR 2020 - [c51]William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney:
Revisiting Fundamentals of Experience Replay. ICML 2020: 3061-3071 - [c50]Samarth Sinha, Han Zhang, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Augustus Odena:
Small-GAN: Speeding up GAN Training using Core-Sets. ICML 2020: 9005-9015 - [c49]Daniel D. Johnson, Hugo Larochelle, Daniel Tarlow:
Learning Graph Structure With A Finite-State Automaton Layer. NeurIPS 2020 - [c48]David Bieber, Charles Sutton, Hugo Larochelle, Daniel Tarlow:
Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks. NeurIPS 2020 - [c47]Tong Che, Ruixiang Zhang, Jascha Sohl-Dickstein, Hugo Larochelle, Liam Paull, Yuan Cao, Yoshua Bengio:
Your GAN is Secretly an Energy-based Model and You Should Use Discriminator Driven Latent Sampling. NeurIPS 2020 - [c46]Samarth Sinha, Animesh Garg, Hugo Larochelle:
Curriculum By Smoothing. NeurIPS 2020 - 2019
- [c45]Laurent Dinh, Jascha Sohl-Dickstein, Razvan Pascanu, Hugo Larochelle:
A RAD approach to deep mixture models. DGS@ICLR 2019 - [c44]Anirudh Goyal, Philemon Brakel, William Fedus, Soumye Singhal, Timothy P. Lillicrap, Sergey Levine, Hugo Larochelle, Yoshua Bengio:
Recall Traces: Backtracking Models for Efficient Reinforcement Learning. ICLR (Poster) 2019 - [c43]Anirudh Goyal, Riashat Islam, Daniel Strouse, Zafarali Ahmed, Hugo Larochelle, Matthew M. Botvinick, Yoshua Bengio, Sergey Levine:
InfoBot: Transfer and Exploration via the Information Bottleneck. ICLR (Poster) 2019 - 2018
- [c42]Simon Brodeur, Ethan Perez, Ankesh Anand, Florian Golemo, Luca Celotti, Florian Strub, Jean Rouat, Hugo Larochelle, Aaron C. Courville:
HoME: a Household Multimodal Environment. ICLR (Workshop) 2018 - [c41]Sachin Ravi, Hugo Larochelle:
Meta-Learning for Batch Mode Active Learning. ICLR (Workshop) 2018 - [c40]Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel:
Meta-Learning for Semi-Supervised Few-Shot Classification. ICLR (Poster) 2018 - 2017
- [c39]Harm de Vries, Florian Strub, Sarath Chandar, Olivier Pietquin, Hugo Larochelle, Aaron C. Courville:
GuessWhat?! Visual Object Discovery through Multi-modal Dialogue. CVPR 2017: 4466-4475 - [c38]Loris Bazzani, Hugo Larochelle, Lorenzo Torresani:
Recurrent Mixture Density Network for Spatiotemporal Visual Attention. ICLR (Poster) 2017 - [c37]Sachin Ravi, Hugo Larochelle:
Optimization as a Model for Few-Shot Learning. ICLR 2017 - [c36]Philippe Poulin, Marc-Alexandre Côté, Jean-Christophe Houde, Laurent Petit, Peter F. Neher, Klaus H. Maier-Hein, Hugo Larochelle, Maxime Descoteaux:
Learn to Track: Deep Learning for Tractography. MICCAI (1) 2017: 540-547 - [c35]Harm de Vries, Florian Strub, Jérémie Mary, Hugo Larochelle, Olivier Pietquin, Aaron C. Courville:
Modulating early visual processing by language. NIPS 2017: 6594-6604 - [c34]Manasi Vartak, Arvind Thiagarajan, Conrado Miranda, Jeshua Bratman, Hugo Larochelle:
A Meta-Learning Perspective on Cold-Start Recommendations for Items. NIPS 2017: 6904-6914 - 2016
- [c33]Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, Ole Winther:
Autoencoding beyond pixels using a learned similarity metric. ICML 2016: 1558-1566 - [c32]Amjad Almahairi, Nicolas Ballas, Tim Cooijmans, Yin Zheng, Hugo Larochelle, Aaron C. Courville:
Dynamic Capacity Networks. ICML 2016: 2549-2558 - 2015
- [c31]Alexandre Allauzen, Edward Grefenstette, Karl Moritz Hermann, Hugo Larochelle, Scott Wen-tau Yih:
Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality. CVSC 2015 - [c30]Li Yao, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher J. Pal, Hugo Larochelle, Aaron C. Courville:
Describing Videos by Exploiting Temporal Structure. ICCV 2015: 4507-4515 - [c29]Mathieu Germain, Karol Gregor, Iain Murray, Hugo Larochelle:
MADE: Masked Autoencoder for Distribution Estimation. ICML 2015: 881-889 - [c28]Francis Bisson, Hugo Larochelle, Froduald Kabanza:
Using a Recursive Neural Network to Learn an Agent's Decision Model for Plan Recognition. IJCAI 2015: 918-924 - [c27]Mohammad Havaei, Francis Dutil, Chris Pal, Hugo Larochelle, Pierre-Marc Jodoin:
A Convolutional Neural Network Approach to Brain Tumor Segmentation. Brainles@MICCAI 2015: 195-208 - 2014
- [c26]Yin Zheng, Yu-Jin Zhang, Hugo Larochelle:
Topic Modeling of Multimodal Data: An Autoregressive Approach. CVPR 2014: 1370-1377 - [c25]Benigno Uria, Iain Murray, Hugo Larochelle:
A Deep and Tractable Density Estimator. ICML 2014: 467-475 - [c24]Alexandre Lacoste, Mario Marchand, François Laviolette, Hugo Larochelle:
Agnostic Bayesian Learning of Ensembles. ICML 2014: 611-619 - [c23]Mohammad Havaei, Pierre-Marc Jodoin, Hugo Larochelle:
Efficient Interactive Brain Tumor Segmentation as Within-Brain kNN Classification. ICPR 2014: 556-561 - [c22]Laurent Charlin, Richard S. Zemel, Hugo Larochelle:
Leveraging user libraries to bootstrap collaborative filtering. KDD 2014: 173-182 - [c21]A. P. Sarath Chandar, Stanislas Lauly, Hugo Larochelle, Mitesh M. Khapra, Balaraman Ravindran, Vikas C. Raykar, Amrita Saha:
An Autoencoder Approach to Learning Bilingual Word Representations. NIPS 2014: 1853-1861 - [c20]Alexandre Lacoste, Hugo Larochelle, Mario Marchand, François Laviolette:
Sequential Model-Based Ensemble Optimization. UAI 2014: 440-448 - 2013
- [c19]Benigno Uria, Iain Murray, Hugo Larochelle:
RNADE: The real-valued neural autoregressive density-estimator. NIPS 2013: 2175-2183 - 2012
- [c18]Maksims Volkovs, Hugo Larochelle, Richard S. Zemel:
Learning to rank by aggregating expert preferences. CIKM 2012: 843-851 - [c17]George E. Dahl, Ryan Prescott Adams, Hugo Larochelle:
Training Restricted Boltzmann Machines on Word Observations. ICML 2012 - [c16]Hugo Larochelle, Stanislas Lauly:
A Neural Autoregressive Topic Model. NIPS 2012: 2717-2725 - [c15]Jasper Snoek, Hugo Larochelle, Ryan P. Adams:
Practical Bayesian Optimization of Machine Learning Algorithms. NIPS 2012: 2960-2968 - [c14]Jasper Snoek, Ryan Prescott Adams, Hugo Larochelle:
On Nonparametric Guidance for Learning Autoencoder Representations. AISTATS 2012: 1073-1080 - 2011
- [c13]Loris Bazzani, Nando de Freitas, Hugo Larochelle, Vittorio Murino, Jo-Anne Ting:
Learning attentional policies for tracking and recognition in video with deep networks. ICML 2011: 937-944 - [c12]Jérôme Louradour, Hugo Larochelle:
Classification of Sets using Restricted Boltzmann Machines. UAI 2011: 463-470 - [c11]Volodymyr Mnih, Hugo Larochelle, Geoffrey E. Hinton:
Conditional Restricted Boltzmann Machines for Structured Output Prediction. UAI 2011: 514-522 - [c10]Hugo Larochelle, Iain Murray:
The Neural Autoregressive Distribution Estimator. AISTATS 2011: 29-37 - 2010
- [c9]Hugo Larochelle, Geoffrey E. Hinton:
Learning to combine foveal glimpses with a third-order Boltzmann machine. NIPS 2010: 1243-1251 - [c8]Ruslan Salakhutdinov, Hugo Larochelle:
Efficient Learning of Deep Boltzmann Machines. AISTATS 2010: 693-700 - 2009
- [c7]Hugo Larochelle, Dumitru Erhan, Pascal Vincent:
Deep Learning using Robust Interdependent Codes. AISTATS 2009: 312-319 - 2008
- [c6]Hugo Larochelle, Dumitru Erhan, Yoshua Bengio:
Zero-data Learning of New Tasks. AAAI 2008: 646-651 - [c5]Hugo Larochelle, Yoshua Bengio:
Classification using discriminative restricted Boltzmann machines. ICML 2008: 536-543 - [c4]Pascal Vincent, Hugo Larochelle, Yoshua Bengio, Pierre-Antoine Manzagol:
Extracting and composing robust features with denoising autoencoders. ICML 2008: 1096-1103 - 2007
- [c3]Hugo Larochelle, Dumitru Erhan, Aaron C. Courville, James Bergstra, Yoshua Bengio:
An empirical evaluation of deep architectures on problems with many factors of variation. ICML 2007: 473-480 - 2006
- [c2]Yoshua Bengio, Pascal Lamblin, Dan Popovici, Hugo Larochelle:
Greedy Layer-Wise Training of Deep Networks. NIPS 2006: 153-160 - 2005
- [c1]Yoshua Bengio, Hugo Larochelle, Pascal Vincent:
Non-Local Manifold Parzen Windows. NIPS 2005: 115-122
Parts in Books or Collections
- 2017
- [p2]Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor S. Lempitsky:
Domain-Adversarial Training of Neural Networks. Domain Adaptation in Computer Vision Applications 2017: 189-209 - 2016
- [p1]Mohammad Havaei, Nicolas Guizard, Hugo Larochelle, Pierre-Marc Jodoin:
Deep Learning Trends for Focal Brain Pathology Segmentation in MRI. Machine Learning for Health Informatics 2016: 125-148
Editorship
- 2020
- [e6]Hugo Larochelle, Marc'Aurelio Ranzato, Raia Hadsell, Maria-Florina Balcan, Hsuan-Tien Lin:
Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual. 2020 [contents] - 2019
- [e5]Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alché-Buc, Emily B. Fox, Roman Garnett:
Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada. 2019 [contents] - 2018
- [e4]Samy Bengio, Hanna M. Wallach, Hugo Larochelle, Kristen Grauman, Nicolò Cesa-Bianchi, Roman Garnett:
Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada. 2018 [contents] - 2015
- [e3]Alexandre Allauzen, Edward Grefenstette, Karl Moritz Hermann, Hugo Larochelle, Scott Wen-tau Yih:
Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality, CVSC 2015, Beijing, China, July 26-31, 2015. Association for Computational Linguistics 2015 [contents] - 2014
- [e2]Alexandre Allauzen, Raffaella Bernardi, Edward Grefenstette, Hugo Larochelle, Christopher D. Manning, Scott Wen-tau Yih:
Proceedings of the 2nd Workshop on Continuous Vector Space Models and their Compositionality, CVSC@EACL 2014, Gothenburg, Sweden, April 26-30, 2014. Association for Computational Linguistics 2014, ISBN 978-1-937284-94-7 [contents] - 2013
- [e1]Alexandre Allauzen, Hugo Larochelle, Christopher D. Manning, Richard Socher:
Proceedings of the Workshop on Continuous Vector Space Models and their Compositionality, CVSM@ACL 2013, Sofia, Bulgaria, August 9, 2013. Association for Computational Linguistics 2013, ISBN 978-1-937284-67-1 [contents]
Informal and Other Publications
- 2024
- [i77]Rishabh Agarwal, Avi Singh, Lei M. Zhang, Bernd Bohnet, Stephanie C. Y. Chan, Ankesh Anand, Zaheer Abbas, Azade Nova, John D. Co-Reyes, Eric Chu, Feryal M. P. Behbahani, Aleksandra Faust, Hugo Larochelle:
Many-Shot In-Context Learning. CoRR abs/2404.11018 (2024) - 2023
- [i76]Mélisande Teng, Amna Elmustafa, Benjamin Akera, Hugo Larochelle, David Rolnick:
Bird Distribution Modelling using Remote Sensing and Citizen Science data. CoRR abs/2305.01079 (2023) - [i75]Mélisande Teng, Amna Elmustafa, Benjamin Akera, Yoshua Bengio, Hager Radi Abdelwahed, Hugo Larochelle, David Rolnick:
SatBird: Bird Species Distribution Modeling with Remote Sensing and Citizen Science Data. CoRR abs/2311.00936 (2023) - [i74]Vincent Dumoulin, Daniel D. Johnson, Pablo Samuel Castro, Hugo Larochelle, Yann N. Dauphin:
A density estimation perspective on learning from pairwise human preferences. CoRR abs/2311.14115 (2023) - [i73]Vedant Shah, Frederik Träuble, Ashish Malik, Hugo Larochelle, Michael Mozer, Sanjeev Arora, Yoshua Bengio, Anirudh Goyal:
Unlearning via Sparse Representations. CoRR abs/2311.15268 (2023) - 2022
- [i72]Utku Evci, Vincent Dumoulin, Hugo Larochelle, Michael C. Mozer:
Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning. CoRR abs/2201.03529 (2022) - [i71]Hattie Zhou, Ankit Vani, Hugo Larochelle, Aaron C. Courville:
Fortuitous Forgetting in Connectionist Networks. CoRR abs/2202.00155 (2022) - [i70]David Bieber, Rishab Goel, Daniel Zheng, Hugo Larochelle, Daniel Tarlow:
Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions. CoRR abs/2203.03771 (2022) - [i69]Arman Afrasiyabi, Hugo Larochelle, Jean-François Lalonde, Christian Gagné:
Matching Feature Sets for Few-Shot Image Classification. CoRR abs/2204.00949 (2022) - [i68]Disha Shrivastava, Hugo Larochelle, Daniel Tarlow:
Repository-Level Prompt Generation for Large Language Models of Code. CoRR abs/2206.12839 (2022) - [i67]Hattie Zhou, Azade Nova, Hugo Larochelle, Aaron C. Courville, Behnam Neyshabur, Hanie Sedghi:
Teaching Algorithmic Reasoning via In-context Learning. CoRR abs/2211.09066 (2022) - 2021
- [i66]Cinjon Resnick, Or Litany, Cosmas Heiß, Hugo Larochelle, Joan Bruna, Kyunghyun Cho:
Self-Supervised Equivariant Scene Synthesis from Video. CoRR abs/2102.00863 (2021) - [i65]Prashanth Vijayaraghavan, Hugo Larochelle, Deb Roy:
Interpretable Multi-Modal Hate Speech Detection. CoRR abs/2103.01616 (2021) - [i64]Vincent Dumoulin, Neil Houlsby, Utku Evci, Xiaohua Zhai, Ross Goroshin, Sylvain Gelly, Hugo Larochelle:
Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark. CoRR abs/2104.02638 (2021) - [i63]Eleni Triantafillou, Hugo Larochelle, Richard S. Zemel, Vincent Dumoulin:
Learning a Universal Template for Few-shot Dataset Generalization. CoRR abs/2105.07029 (2021) - [i62]Disha Shrivastava, Hugo Larochelle, Daniel Tarlow:
Learning to Combine Per-Example Solutions for Neural Program Synthesis. CoRR abs/2106.07175 (2021) - [i61]Cristina Nader Vasconcelos, Hugo Larochelle, Vincent Dumoulin, Rob Romijnders, Nicolas Le Roux, Ross Goroshin:
Impact of Aliasing on Generalization in Deep Convolutional Networks. CoRR abs/2108.03489 (2021) - 2020
- [i60]William Fedus, Dibya Ghosh, John D. Martin, Marc G. Bellemare, Yoshua Bengio, Hugo Larochelle:
On Catastrophic Interference in Atari 2600 Games. CoRR abs/2002.12499 (2020) - [i59]Samarth Sinha, Animesh Garg, Hugo Larochelle:
Curriculum By Texture. CoRR abs/2003.01367 (2020) - [i58]Samarth Sinha, Homanga Bharadhwaj, Anirudh Goyal, Hugo Larochelle, Animesh Garg, Florian Shkurti:
DIBS: Diversity inducing Information Bottleneck in Model Ensembles. CoRR abs/2003.04514 (2020) - [i57]Tong Che, Ruixiang Zhang, Jascha Sohl-Dickstein, Hugo Larochelle, Liam Paull, Yuan Cao, Yoshua Bengio:
Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling. CoRR abs/2003.06060 (2020) - [i56]Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Larivière, Alina Beygelzimer, Florence d'Alché-Buc, Emily B. Fox, Hugo Larochelle:
Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program). CoRR abs/2003.12206 (2020) - [i55]Lu Liu, William L. Hamilton, Guodong Long, Jing Jiang, Hugo Larochelle:
A Universal Representation Transformer Layer for Few-Shot Image Classification. CoRR abs/2006.11702 (2020) - [i54]Daniel D. Johnson, Hugo Larochelle, Daniel Tarlow:
Learning Graph Structure With A Finite-State Automaton Layer. CoRR abs/2007.04929 (2020) - [i53]William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland, Will Dabney:
Revisiting Fundamentals of Experience Replay. CoRR abs/2007.06700 (2020) - [i52]David Bieber, Charles Sutton, Hugo Larochelle, Daniel Tarlow:
Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks. CoRR abs/2010.12621 (2020) - [i51]Cinjon Resnick, Or Litany, Hugo Larochelle, Joan Bruna, Kyunghyun Cho:
Learned Equivariant Rendering without Transformation Supervision. CoRR abs/2011.05787 (2020) - [i50]Cristina Nader Vasconcelos, Hugo Larochelle, Vincent Dumoulin, Nicolas Le Roux, Ross Goroshin:
An Effective Anti-Aliasing Approach for Residual Networks. CoRR abs/2011.10675 (2020) - 2019
- [i49]Anirudh Goyal, Riashat Islam, Daniel Strouse, Zafarali Ahmed, Matthew M. Botvinick, Hugo Larochelle, Sergey Levine, Yoshua Bengio:
InfoBot: Transfer and Exploration via the Information Bottleneck. CoRR abs/1901.10902 (2019) - [i48]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, Michael Bowling:
The Hanabi Challenge: A New Frontier for AI Research. CoRR abs/1902.00506 (2019) - [i47]William Fedus, Carles Gelada, Yoshua Bengio, Marc G. Bellemare, Hugo Larochelle:
Hyperbolic Discounting and Learning over Multiple Horizons. CoRR abs/1902.06865 (2019) - [i46]Gabriel Huang, Hugo Larochelle, Simon Lacoste-Julien:
Centroid Networks for Few-Shot Clustering and Unsupervised Few-Shot Classification. CoRR abs/1902.08605 (2019) - [i45]Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, Hugo Larochelle:
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples. CoRR abs/1903.03096 (2019) - [i44]Laurent Dinh, Jascha Sohl-Dickstein, Razvan Pascanu, Hugo Larochelle:
A RAD approach to deep mixture models. CoRR abs/1903.07714 (2019) - [i43]Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Chris Pal, Yoshua Bengio:
Learning Neural Causal Models from Unknown Interventions. CoRR abs/1910.01075 (2019) - [i42]Samarth Sinha, Han Zhang, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Augustus Odena:
Small-GAN: Speeding Up GAN Training Using Core-sets. CoRR abs/1910.13540 (2019) - [i41]Vishal Jain, William Fedus, Hugo Larochelle, Doina Precup, Marc G. Bellemare:
Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction. CoRR abs/1911.12511 (2019) - 2018
- [i40]Valentin Thomas, Emmanuel Bengio, William Fedus, Jules Pondard, Philippe Beaudoin, Hugo Larochelle, Joelle Pineau, Doina Precup, Yoshua Bengio:
Disentangling the independently controllable factors of variation by interacting with the world. CoRR abs/1802.09484 (2018) - [i39]Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell, Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richard S. Zemel:
Meta-Learning for Semi-Supervised Few-Shot Classification. CoRR abs/1803.00676 (2018) - [i38]Anirudh Goyal, Philemon Brakel, William Fedus, Timothy P. Lillicrap, Sergey Levine, Hugo Larochelle, Yoshua Bengio:
Recall Traces: Backtracking Models for Efficient Reinforcement Learning. CoRR abs/1804.00379 (2018) - [i37]Massimo Caccia, Lucas Caccia, William Fedus, Hugo Larochelle, Joelle Pineau, Laurent Charlin:
Language GANs Falling Short. CoRR abs/1811.02549 (2018) - [i36]Ankesh Anand, Eugene Belilovsky, Kyle Kastner, Hugo Larochelle, Aaron C. Courville:
Blindfold Baselines for Embodied QA. CoRR abs/1811.05013 (2018) - 2017
- [i35]Harm de Vries, Florian Strub, Jérémie Mary, Hugo Larochelle, Olivier Pietquin, Aaron C. Courville:
Modulating early visual processing by language. CoRR abs/1707.00683 (2017) - [i34]Bart van Merriënboer, Amartya Sanyal, Hugo Larochelle, Yoshua Bengio:
Multiscale sequence modeling with a learned dictionary. CoRR abs/1707.00762 (2017) - [i33]Simon Brodeur, Ethan Perez, Ankesh Anand, Florian Golemo, Luca Celotti, Florian Strub, Jean Rouat, Hugo Larochelle, Aaron C. Courville:
HoME: a Household Multimodal Environment. CoRR abs/1711.11017 (2017) - 2016
- [i32]Stanislas Lauly, Yin Zheng, Alexandre Allauzen, Hugo Larochelle:
Document Neural Autoregressive Distribution Estimation. CoRR abs/1603.05962 (2016) - [i31]Loris Bazzani, Hugo Larochelle, Lorenzo Torresani:
Recurrent Mixture Density Network for Spatiotemporal Visual Attention. CoRR abs/1603.08199 (2016) - [i30]Benigno Uria, Marc-Alexandre Côté, Karol Gregor, Iain Murray, Hugo Larochelle:
Neural Autoregressive Distribution Estimation. CoRR abs/1605.02226 (2016) - [i29]Anna Rohrbach, Atousa Torabi, Marcus Rohrbach, Niket Tandon, Christopher J. Pal, Hugo Larochelle, Aaron C. Courville, Bernt Schiele:
Movie Description. CoRR abs/1605.03705 (2016) - [i28]Sarath Chandar, Sungjin Ahn, Hugo Larochelle, Pascal Vincent, Gerald Tesauro, Yoshua Bengio:
Hierarchical Memory Networks. CoRR abs/1605.07427 (2016) - [i27]David Krueger, Tegan Maharaj, János Kramár, Mohammad Pezeshki, Nicolas Ballas, Nan Rosemary Ke, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle, Aaron C. Courville, Chris Pal:
Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations. CoRR abs/1606.01305 (2016) - [i26]Mohammad Havaei, Nicolas Guizard, Hugo Larochelle, Pierre-Marc Jodoin:
Deep learning trends for focal brain pathology segmentation in MRI. CoRR abs/1607.05258 (2016) - [i25]Harm de Vries, Florian Strub, Sarath Chandar, Olivier Pietquin, Hugo Larochelle, Aaron C. Courville:
GuessWhat?! Visual object discovery through multi-modal dialogue. CoRR abs/1611.08481 (2016) - 2015
- [i24]Marc-Alexandre Côté, Hugo Larochelle:
An Infinite Restricted Boltzmann Machine. CoRR abs/1502.02476 (2015) - [i23]Mathieu Germain, Karol Gregor, Iain Murray, Hugo Larochelle:
MADE: Masked Autoencoder for Distribution Estimation. CoRR abs/1502.03509 (2015) - [i22]Li Yao, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher Joseph Pal, Hugo Larochelle, Aaron C. Courville:
Video Description Generation Incorporating Spatio-Temporal Features and a Soft-Attention Mechanism. CoRR abs/1502.08029 (2015) - [i21]Atousa Torabi, Christopher J. Pal, Hugo Larochelle, Aaron C. Courville:
Using Descriptive Video Services to Create a Large Data Source for Video Annotation Research. CoRR abs/1503.01070 (2015) - [i20]Sarath Chandar, Mitesh M. Khapra, Hugo Larochelle, Balaraman Ravindran:
Correlational Neural Networks. CoRR abs/1504.07225 (2015) - [i19]Mohammad Havaei, Axel Davy, David Warde-Farley, Antoine Biard, Aaron C. Courville, Yoshua Bengio, Chris Pal, Pierre-Marc Jodoin, Hugo Larochelle:
Brain Tumor Segmentation with Deep Neural Networks. CoRR abs/1505.03540 (2015) - [i18]Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, Victor S. Lempitsky:
Domain-Adversarial Training of Neural Networks. CoRR abs/1505.07818 (2015) - [i17]Mohammad Havaei, Hugo Larochelle, Philippe Poulin, Pierre-Marc Jodoin:
Within-Brain Classification for Brain Tumor Segmentation. CoRR abs/1510.01344 (2015) - [i16]Amjad Almahairi, Nicolas Ballas, Tim Cooijmans, Yin Zheng, Hugo Larochelle, Aaron C. Courville:
Dynamic Capacity Networks. CoRR abs/1511.07838 (2015) - 2014
- [i15]Stanislas Lauly, Alex Boulanger, Hugo Larochelle:
Learning Multilingual Word Representations using a Bag-of-Words Autoencoder. CoRR abs/1401.1803 (2014) - [i14]Alexandre Lacoste, Hugo Larochelle, François Laviolette, Mario Marchand:
Sequential Model-Based Ensemble Optimization. CoRR abs/1402.0796 (2014) - [i13]A. P. Sarath Chandar, Stanislas Lauly, Hugo Larochelle, Mitesh M. Khapra, Balaraman Ravindran, Vikas C. Raykar, Amrita Saha:
An Autoencoder Approach to Learning Bilingual Word Representations. CoRR abs/1402.1454 (2014) - [i12]Yin Zheng, Yu-Jin Zhang, Hugo Larochelle:
A Deep and Autoregressive Approach for Topic Modeling of Multimodal Data. CoRR abs/1409.3970 (2014) - [i11]Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand:
Domain-Adversarial Neural Networks. CoRR abs/1412.4446 (2014) - 2013
- [i10]Yin Zheng, Yu-Jin Zhang, Hugo Larochelle:
A Supervised Neural Autoregressive Topic Model for Simultaneous Image Classification and Annotation. CoRR abs/1305.5306 (2013) - [i9]Benigno Uria, Iain Murray, Hugo Larochelle:
NADE: The real-valued neural autoregressive density-estimator. CoRR abs/1306.0186 (2013) - [i8]Benigno Uria, Iain Murray, Hugo Larochelle:
A Deep and Tractable Density Estimator. CoRR abs/1310.1757 (2013) - 2012
- [i7]Volodymyr Mnih, Hugo Larochelle, Geoffrey E. Hinton:
Conditional Restricted Boltzmann Machines for Structured Output Prediction. CoRR abs/1202.3748 (2012) - [i6]George E. Dahl, Ryan Prescott Adams, Hugo Larochelle:
Training Restricted Boltzmann Machines on Word Observations. CoRR abs/1202.5695 (2012) - [i5]Jasper Snoek, Hugo Larochelle, Ryan Prescott Adams:
Practical Bayesian Optimization of Machine Learning Algorithms. CoRR abs/1206.2944 (2012) - 2011
- [i4]Michael I. Mandel, Razvan Pascanu, Hugo Larochelle, Yoshua Bengio:
Autotagging music with conditional restricted Boltzmann machines. CoRR abs/1103.2832 (2011) - [i3]Jérôme Louradour, Hugo Larochelle:
Classification of Sets using Restricted Boltzmann Machines. CoRR abs/1103.4896 (2011) - [i2]Maksims Volkovs, Hugo Larochelle, Richard S. Zemel:
Loss-sensitive Training of Probabilistic Conditional Random Fields. CoRR abs/1107.1805 (2011) - [i1]Misha Denil, Loris Bazzani, Hugo Larochelle, Nando de Freitas:
Learning where to Attend with Deep Architectures for Image Tracking. CoRR abs/1109.3737 (2011)
Coauthor Index
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