Hugo Larochelle

Mila > About Mila > Team > Hugo Larochelle
Core Industry Member
Hugo Larochelle
Adjunct Professor, Université de Montréal, Google
Hugo Larochelle

I am the lead of the Google Brain team in Montreal, adjunct professor at Université de Montréal and a Canada CIFAR Chair. My research focuses on the study and development of deep learning algorithms.

For additional information on me and my research, consider the following links:

Publications

2021-08

Impact of Aliasing on Generalization in Deep Convolutional Networks
Cristina Vasconcelos, Hugo Larochelle, Vincent Dumoulin, Rob Romijnders, Nicolas Le Roux and Ross Goroshin
arXiv preprint arXiv:2108.03489
(2021-08-07)
arxiv.orgPDF

2021-07

Learning a Universal Template for Few-shot Dataset Generalization
Eleni Triantafillou, Hugo Larochelle, Richard Zemel and Vincent Dumoulin

2021-06

Learning to Combine Per-Example Solutions for Neural Program Synthesis.
Disha Shrivastava, Hugo Larochelle and Daniel Tarlow
arXiv preprint arXiv:2106.07175
(2021-06-14)
dblp.uni-trier.dePDF
A Unified Few-Shot Classification Benchmark to Compare Transfer and Meta Learning Approaches
Vincent Dumoulin, Neil Houlsby, Utku Evci, Xiaohua Zhai, Ross Goroshin, Sylvain Gelly and Hugo Larochelle
NEURIPS 2021
(2021-06-07)
openreview.net

2021-05

Learning Flexible Classifiers with Shot-CONditional Episodic (SCONE) Training
Eleni Triantafillou, Vincent Dumoulin, Hugo Larochelle and Richard Zemel
(venue unknown)
(2021-05-04)
openreview.netPDF
Dependency Structure Discovery from Interventions
Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Bernhard Schölkopf, Michael Curtis Mozer, Hugo Larochelle, Christopher Pal and Yoshua Bengio
(venue unknown)
(2021-05-04)
openreview.netPDF
Uniform Priors for Data-Efficient Transfer
Samarth Sinha, Karsten Roth, Anirudh Goyal, Marzyeh Ghassemi, Hugo Larochelle and Animesh Garg
arXiv e-prints
(2021-05-04)
ui.adsabs.harvard.eduPDF
A Universal Representation Transformer Layer for Few-Shot Image Classification
Lu Liu, William L. Hamilton, Guodong Long, Jing Jiang and Hugo Larochelle

2021-04

Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark.
Vincent Dumoulin, Neil Houlsby, Utku Evci, Xiaohua Zhai, Ross Goroshin, Sylvain Gelly and Hugo Larochelle
arXiv preprint arXiv:2104.02638
(2021-04-06)
ui.adsabs.harvard.eduPDF

2021-03

Interpretable Multi-Modal Hate Speech Detection.
Prashanth Vijayaraghavan, Hugo Larochelle and Deb Roy
arXiv preprint arXiv:2103.01616
(2021-03-02)
ui.adsabs.harvard.eduPDF

2021-02

Self-Supervised Equivariant Scene Synthesis from Video
Cinjon Resnick, Or Litany, Cosmas Heiß, Hugo Larochelle, Joan Bruna and Kyunghyun Cho
arXiv preprint arXiv:2102.00863
(2021-02-01)
ui.adsabs.harvard.eduPDF

2021-01

Improving Reproducibility in Machine Learning Research(A Report from the NeurIPS 2019 Reproducibility Program)
Joelle Pineau, Philippe Vincent-Lamarre, Koustuv Sinha, Vincent Larivière, Alina Beygelzimer, Florence d'Alché-Buc, Emily B. Fox and Hugo Larochelle

2020-11

An Effective Anti-Aliasing Approach for Residual Networks.
Cristina Vasconcelos, Hugo Larochelle, Vincent Dumoulin, Nicolas Le Roux and Ross Goroshin
arXiv preprint arXiv:2011.10675
(2020-11-20)
ui.adsabs.harvard.eduPDF
Learned Equivariant Rendering without Transformation Supervision.
Cinjon Resnick, Or Litany, Hugo Larochelle, Joan Bruna and Kyunghyun Cho
arXiv preprint arXiv:2011.05787
(2020-11-11)
ui.adsabs.harvard.eduPDF

2020-07

Revisiting Fundamentals of Experience Replay
William Fedus, Prajit Ramachandran, Rishabh Agarwal, Yoshua Bengio, Hugo Larochelle, Mark Rowland and Will Dabney
Small-GAN: Speeding up GAN Training using Core-Sets
Samrath Sinha, Han Zhang, Anirudh Goyal, Yoshua Bengio, Hugo Larochelle and Augustus Odena

2020-04

Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol and Hugo Larochelle
Language GANs Falling Short
Massimo Caccia, Lucas Caccia, William Fedus, Hugo Larochelle, Joelle Pineau and Laurent Charlin
sistemas e métodos para realizar otimização bayesiana
Hugo Larochelle, Kevin Swersky, Richard Zemel, Roland Jasper Snoek and Ryan P Adams
(venue unknown)
(2020-04-28)
lens.org
Algorithmic Improvements for Deep Reinforcement Learning Applied to Interactive Fiction.
Vishal Jain, William Fedus, Hugo Larochelle, Doina Precup and Marc G. Bellemare

2020-03

On-the-Fly Adaptation of Source Code Models using Meta-Learning
Disha Shrivastava, Hugo Larochelle and Daniel Tarlow
arXiv preprint arXiv:2003.11768
(2020-03-26)
ui.adsabs.harvard.eduPDF
Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling
Tong Che, Ruixiang Zhang, Jascha Sohl-Dickstein, Hugo Larochelle, Liam Paull, Yuan Cao and Yoshua Bengio
Diversity inducing Information Bottleneck in Model Ensembles.
Samarth Sinha, Homanga Bharadhwaj, Anirudh Goyal, Hugo Larochelle, Animesh Garg and Florian Shkurti
arXiv preprint arXiv:2003.04514
(2020-03-10)
ui.adsabs.harvard.eduPDF
Curriculum By Texture.
Samarth Sinha, Animesh Garg and Hugo Larochelle
(venue unknown)
(2020-03-03)
dblp.uni-trier.dePDF
The Hanabi Challenge: A New Frontier for AI Research
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
Artificial Intelligence
(2020-03-01)
www.sciencedirect.comPDF

2020-01

DIBS: Diversity Inducing Information Bottleneck in Model Ensembles.
Samarth Sinha, Homanga Bharadhwaj, Anirudh Goyal, Hugo Larochelle, Animesh Garg and Florian Shkurti
AAAI 2020
(2020-01-01)
dblp.uni-trier.de
Acquiring and Predicting Multidimensional Diffusion (MUDI) Data: An Open Challenge
Marco Pizzolato, Marco Palombo, Elisenda Bonet-Carne, Chantal M. W. Tax, Francesco Grussu, Andrada Ianus, Fabian Bogusz, Tomasz Pieciak, Lipeng Ning, Hugo Larochelle, Maxime Descoteaux, Maxime Chamberland, Stefano B. Blumberg, Thomy Mertzanidou, Daniel C. Alexander, Maryam Afzali, Santiago Aja-Fernández, Derek K. Jones, Carl-Fredrik Westin, Yogesh Rathi... (10 more)
(venue unknown)
(2020-01-01)
acuresearchbank.acu.edu.au
Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks
David Bieber, Charles Sutton, Hugo Larochelle and Daniel Tarlow
Few-Shot Learning
(venue unknown)
(2020-01-01)
link.springer.com
Curriculum By Smoothing
Samarth Sinha, Animesh Garg and Hugo Larochelle
Learning Graph Structure With A Finite-State Automaton Layer
Daniel D. Johnson, Hugo Larochelle and Daniel Tarlow
On Catastrophic Interference in Atari 2600 Games
William Fedus, Dibya Ghosh, John D. Martin, Marc G. Bellemare, Yoshua Bengio and Hugo Larochelle
arXiv preprint arXiv:2002.12499
(2020-01-01)
ui.adsabs.harvard.eduPDF

2019-09

Are Few-shot Learning Benchmarks Too Simple ?
(venue unknown)
(2019-09-25)
openreview.netPDF

2019-03

A RAD approach to deep mixture models
Laurent Dinh, Jascha Sohl-Dickstein, Razvan Pascanu and Hugo Larochelle
ICLR 2019
(2019-03-27)
dblp.uni-trier.de
A RAD approach to deep mixture models
Laurent Dinh, Jascha Sohl-Dickstein, Hugo Larochelle and Razvan Pascanu
arXiv preprint arXiv:1903.07714
(2019-03-18)
ui.adsabs.harvard.eduPDF

2019-02

Are Few-Shot Learning Benchmarks too Simple ? Solving them without Task Supervision at Test-Time
arXiv preprint arXiv:1902.08605
(2019-02-22)
ui.adsabs.harvard.eduPDF
Centroid Networks for Few-Shot Clustering and Unsupervised Few-Shot Classification.
(venue unknown)
(2019-02-22)
dblp.uni-trier.dePDF
Hyperbolic Discounting and Learning over Multiple Horizons
William Fedus, Carles Gelada, Yoshua Bengio, Marc G. Bellemare and Hugo Larochelle
arXiv preprint arXiv:1902.06865
(2019-02-19)
ui.adsabs.harvard.eduPDF

2019-01

InfoBot: Transfer and Exploration via the Information Bottleneck
Anirudh Goyal, Riashat Islam, Daniel Strouse, Zafarali Ahmed, Matthew Botvinick, Hugo Larochelle, Yoshua Bengio and Sergey Levine
arXiv preprint arXiv:1901.10902
(2019-01-30)
ui.adsabs.harvard.eduPDF
InfoBot: Structured Exploration in ReinforcementLearning Using Information Bottleneck
Anirudh Goyal, Riashat Islam, Daniel Strouse, Zafarali Ahmed, Matthew Botvinick, Hugo Larochelle, Yoshua Bengio and Sergey Levine
(venue unknown)
(2019-01-01)
research.google
InfoBot: Transfer and Exploration via the Information Bottleneck
Anirudh Goyal, Riashat Islam, Daniel Strouse, Zafarali Ahmed, Hugo Larochelle, Matthew Botvinick, Yoshua Bengio and Sergey Levine
ICLR 2019
(2019-01-01)
dblp.uni-trier.dePDF

2018-11

Blindfold Baselines for Embodied QA.
Ankesh Anand, Eugene Belilovsky, Kyle Kastner, Hugo Larochelle and Aaron C. Courville
arXiv: Computer Vision and Pattern Recognition
(2018-11-12)
ui.adsabs.harvard.eduPDF

2018-09

Recall Traces: Backtracking Models for Efficient Reinforcement Learning
Anirudh Goyal, Philemon Brakel, William Fedus, Soumye Singhal, Timothy P. Lillicrap, Sergey Levine, Hugo Larochelle and Yoshua Bengio

2018-02

Disentangling the independently controllable factors of variation by interacting with the world
Valentin Thomas, Emmanuel Bengio, William Fedus, Jules Pondard, Philippe Beaudoin, Hugo Larochelle, Joelle Pineau, Doina Precup and Yoshua Bengio
arXiv preprint arXiv:1802.09484
(2018-02-26)
ui.adsabs.harvard.eduPDF

2018-01

HoME: a Household Multimodal Environment.
Simon Brodeur, Ethan Perez, Ankesh Anand, Florian Golemo, Luca Celotti, Florian Strub, Jean Rouat, Hugo Larochelle and Aaron C. Courville
ICLR 2018
(2018-01-01)
dblp.uni-trier.dePDF

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