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Cross-Trajectory Representation Learning for Zero-Shot Generalization in RL


Jun 04, 2021
Bogdan Mazoure, Ahmed M. Ahmed, Patrick MacAlpine, R Devon Hjelm, Andrey Kolobov


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Understanding by Understanding Not: Modeling Negation in Language Models


May 07, 2021
Arian Hosseini, Siva Reddy, Dzmitry Bahdanau, R Devon Hjelm, Alessandro Sordoni, Aaron Courville


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Zero-Shot Learning from scratch (ZFS): leveraging local compositional representations


Oct 22, 2020
Tristan Sylvain, Linda Petrini, R Devon Hjelm

* ICML 2019 Workshop on Understanding and Improving General-ization in Deep Learning, Long Beach, California, 2019 Spotlight presentation. arXiv admin note: text overlap with arXiv:1912.12179 

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Implicit Regularization in Deep Learning: A View from Function Space


Aug 03, 2020
Aristide Baratin, Thomas George, César Laurent, R Devon Hjelm, Guillaume Lajoie, Pascal Vincent, Simon Lacoste-Julien

* 24 pages. A preliminary version of this work has been presented at the NeurIPS 2019 Workshops on "Machine Learning with Guarantees" and "Science meets Engineering of Deep Learning" 

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Representation Learning with Video Deep InfoMax


Jul 28, 2020
R Devon Hjelm, Philip Bachman


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Data-Efficient Reinforcement Learning with Momentum Predictive Representations


Jul 12, 2020
Max Schwarzer, Ankesh Anand, Rishab Goel, R Devon Hjelm, Aaron Courville, Philip Bachman

* The first two authors contributed equally to this work 

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Deep Reinforcement and InfoMax Learning


Jun 12, 2020
Bogdan Mazoure, Remi Tachet des Combes, Thang Doan, Philip Bachman, R Devon Hjelm


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Object-Centric Image Generation from Layouts


Mar 16, 2020
Tristan Sylvain, Pengchuan Zhang, Yoshua Bengio, R Devon Hjelm, Shikhar Sharma


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An end-to-end approach for the verification problem: learning the right distance


Feb 21, 2020
Joao Monteiro, Isabela Albuquerque, Jahangir Alam, R Devon Hjelm, Tiago Falk


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Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning


Sep 24, 2019
Thang Doan, Bogdan Mazoure, Audrey Durand, Joelle Pineau, R Devon Hjelm


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Unsupervised State Representation Learning in Atari


Jun 26, 2019
Ankesh Anand, Evan Racah, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre Côté, R Devon Hjelm

* v2 fixes references to material in the appendix 

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Learning Representations by Maximizing Mutual Information Across Views


Jun 03, 2019
Philip Bachman, R Devon Hjelm, William Buchwalter


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Batch weight for domain adaptation with mass shift


May 29, 2019
Mikołaj Bińkowski, R Devon Hjelm, Aaron Courville


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Leveraging exploration in off-policy algorithms via normalizing flows


May 16, 2019
Bogdan Mazoure, Thang Doan, Audrey Durand, R Devon Hjelm, Joelle Pineau


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Prediction of Progression to Alzheimer's disease with Deep InfoMax


May 01, 2019
Alex Fedorov, R Devon Hjelm, Anees Abrol, Zening Fu, Yuhui Du, Sergey Plis, Vince D. Calhoun

* Accepted to 2019 IEEE Biomedical and Health Informatics (BHI) as a conference paper 

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Spatio-Temporal Deep Graph Infomax


Apr 12, 2019
Felix L. Opolka, Aaron Solomon, Cătălina Cangea, Petar Veličković, Pietro Liò, R Devon Hjelm

* 6 pages, 2 figures, Representation Learning on Graphs and Manifolds Workshop of the International Conference on Learning Representations (ICLR) 

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Adversarial Mixup Resynthesizers


Apr 04, 2019
Christopher Beckham, Sina Honari, Alex Lamb, Vikas Verma, Farnoosh Ghadiri, R Devon Hjelm, Christopher Pal


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Learning deep representations by mutual information estimation and maximization


Oct 03, 2018
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, Yoshua Bengio


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Deep Graph Infomax


Sep 27, 2018
Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R Devon Hjelm

* Under review as a conference paper at ICLR 2019. 15 pages, 8 figures 

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On-line Adaptative Curriculum Learning for GANs


Sep 12, 2018
Thang Doan, Joao Monteiro, Isabela Albuquerque, Bogdan Mazoure, Audrey Durand, Joelle Pineau, R Devon Hjelm


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Spatio-temporal Dynamics of Intrinsic Networks in Functional Magnetic Imaging Data Using Recurrent Neural Networks


Aug 27, 2018
R Devon Hjelm, Eswar Damaraju, Kyunghyun Cho, Helmut Laufs, Sergey M. Plis, Vince Calhoun

* Accepted to Frontiers of Neuroscience 

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Variance Regularizing Adversarial Learning


Aug 19, 2018
Karan Grewal, R Devon Hjelm, Yoshua Bengio

* Method is out of date and some results are incorrect 

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MINE: Mutual Information Neural Estimation


Jun 07, 2018
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeswar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, R Devon Hjelm

* ICML 2018 
* 19 pages, 6 figures 

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Boundary-Seeking Generative Adversarial Networks


Feb 21, 2018
R Devon Hjelm, Athul Paul Jacob, Tong Che, Adam Trischler, Kyunghyun Cho, Yoshua Bengio


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Iterative Refinement of the Approximate Posterior for Directed Belief Networks


Feb 20, 2018
R Devon Hjelm, Kyunghyun Cho, Junyoung Chung, Russ Salakhutdinov, Vince Calhoun, Nebojsa Jojic


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ACtuAL: Actor-Critic Under Adversarial Learning


Nov 13, 2017
Anirudh Goyal, Nan Rosemary Ke, Alex Lamb, R Devon Hjelm, Chris Pal, Joelle Pineau, Yoshua Bengio


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Maximum-Likelihood Augmented Discrete Generative Adversarial Networks


Feb 26, 2017
Tong Che, Yanran Li, Ruixiang Zhang, R Devon Hjelm, Wenjie Li, Yangqiu Song, Yoshua Bengio

* 11 pages, 3 figures 

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