Hugo Larochelle

Membre Industriel Principal
Hugo Larochelle
Professeur associé, Université de Montréal, Google
Hugo Larochelle

Je suis responsable de l’équipe Google Brain basée à Montréal, professeur associé à l’Université de Montréal et Chaire en IA CIFAR-Canada. Ma recherche se spécialise dans l’étude et le développement de méthodes d’apprentissage profond.

Voici quelques liens utiles pour obtenir plus d’information sur moi et mes activités de recherche:

Publications

2020-10

Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks.
David Bieber, Charles Sutton, Hugo Larochelle and Daniel Tarlow
arXiv: Learning
(2020-10-26)
arxiv.orgPDF
Uniform Priors for Data-Efficient Transfer.
Samarth Sinha, Karsten Roth, Anirudh Goyal, Marzyeh Ghassemi, Hugo Larochelle and Animesh Garg
arXiv: Learning
(2020-10-13)
arxiv.orgPDF

2020-07

Are Few-Shot Learning Benchmarks too Simple ? Solving them without Task Supervision at Test-Time.
arXiv: Learning
(2020-07-27)
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
Learning Graph Structure With A Finite-State Automaton Layer.
Daniel D. Johnson, Hugo Larochelle and Daniel Tarlow
arXiv: Learning
(2020-07-09)
dblp.uni-trier.dePDF

2020-06

A Universal Representation Transformer Layer for Few-Shot Image Classification.
Lu Liu, William Hamilton, Guodong Long, Jing Jiang and Hugo Larochelle
arXiv preprint arXiv:2006.11702
(2020-06-21)
dblp.uni-trier.dePDF

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

2020-03

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
arXiv preprint arXiv:2003.12206
(2020-03-27)
dblp.uni-trier.dePDF
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)
dblp.uni-trier.dePDF
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
arXiv preprint arXiv:2003.06060
(2020-03-12)
dblp.uni-trier.dePDF
DIBS: 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)
dblp.uni-trier.dePDF
Curriculum By Smoothing
Samarth Sinha, Animesh Garg and Hugo Larochelle
arXiv preprint arXiv:2003.01367
(2020-03-03)
arxiv.orgPDF
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

2020-02

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
(2020-02-28)
ui.adsabs.harvard.eduPDF
Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction
Vishal Jain, Liam Fedus, Hugo Larochelle, Doina Precup and Marc G. Bellemare
AAAI 2020
(2020-02-07)
aaai.orgPDF

2020-01

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)
link.springer.com
Few-Shot Learning
(venue unknown)
(2020-01-01)
PDF

2019-11

Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction
Vishal Jain, William Fedus, Hugo Larochelle, Doina Precup and Marc G. Bellemare
arXiv preprint arXiv:1911.12511
(2019-11-28)
arxiv.orgPDF

2019-09

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

2019-05

InfoBot: Transfer and Exploration via the Information Bottleneck
Anirudh Goyal Alias Parth Goyal, Riashat Islam, DJ Strouse, Zafarali Ahmed, Hugo Larochelle, Matthew Botvinick, Sergey Levine and Yoshua Bengio
ICLR 2019
(2019-05-06)
iclr.ccPDF
Recall Traces: Backtracking Models for Efficient Reinforcement Learning
Anirudh Goyal Alias Parth Goyal, Philemon Brakel, William Fedus, Soumye Singhal, Timothy Lillicrap, Sergey Levine, Hugo Larochelle and Yoshua Bengio
ICLR 2019
(2019-05-06)
iclr.ccPDF

2019-03

A RAD approach to deep mixture models
Laurent Dinh, Jascha Sohl-Dickstein, Razvan Pascanu and Hugo Larochelle

2019-02

Centroid Networks for Few-Shot Clustering and Unsupervised Few-Shot Classification.
arXiv preprint arXiv:1902.08605
(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)
arxiv.orgPDF
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

2018-11

Blindfold Baselines for Embodied QA.
Ankesh Anand, Eugene Belilovsky, Kyle Kastner, Hugo Larochelle and Aaron C. Courville
arXiv preprint arXiv:1811.05013
(2018-11-12)
ui.adsabs.harvard.eduPDF

2018-04

Recall Traces: Backtracking Models for Efficient Reinforcement Learning
Anirudh Goyal, Philemon Brakel, William Fedus, Timothy Lillicrap, Sergey Levine, Hugo Larochelle and Yoshua Bengio
arXiv preprint arXiv:1804.00379
(2018-04-02)
export.arxiv.orgPDF

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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