Mila > Team > Devon Hjelm

# Devon Hjelm

Affiliate Member
erroneus@gmail.com
https://rdevon.github.io/
Reinforcement Learning (RL)
Information Theory
Natural Language Processing (NLP)
Affiliate Member
Researcher

I am a deep learning Researcher at Microsoft Research, Montreal. I received my PhD at the University of New Mexico, where I studied learning representations of the brain using graphical models. I did a postdoc under Yoshua Bengio doing unsupervised learning, representation learning, and adversarial models. At Microsoft, I study how to learn better representations for scientific analysis and downstream tasks in RL and NLP.

## Publications

### 2021-12

Pretraining Representations for Data-Efficient Reinforcement Learning
Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Charlin, R Devon Hjelm, Philip Bachman and Aaron C. Courville
NEURIPS 2021
(2021-12-06)
papers.nips.cc PDF[Also on arXiv preprint arXiv:2106.04799 (2021-06-09)]
Test Sample Accuracy Scales with Training Sample Density in Neural Networks.
Xu Ji, Razvan Pascanu, Devon Hjelm, Balaji Lakshminarayanan and Andrea Vedaldi
arXiv: Learning
(2021-12-02)
arxiv.org PDF

### 2021-06

Predicting Unreliable Predictions by Shattering a Neural Network
Xu Ji, Razvan Pascanu, R. Devon Hjelm, Andrea Vedaldi, Balaji Lakshminarayanan and Yoshua Bengio
arXiv preprint arXiv:2106.08365
(2021-06-15)
ui.adsabs.harvard.edu
CMIM: Cross-Modal Information Maximization For Medical Imaging
Tristan Sylvain, Francis Dutil, Tess Berthier, Lisa Di Jorio, Margaux Luck, Devon Hjelm and Yoshua Bengio
ICASSP 2021
(2021-06-06)
ieeexplore.ieee.org
Cross-Trajectory Representation Learning for Zero-Shot Generalization in RL.
Bogdan Mazoure, Ahmed M. Ahmed, Patrick MacAlpine, R. Devon Hjelm and Andrey Kolobov
arXiv preprint arXiv:2106.02193
(2021-06-04)
ui.adsabs.harvard.edu PDF

### 2021-05

Understanding by Understanding Not: Modeling Negation in Language Models.
Arian Hosseini, Siva Reddy, Dzmitry Bahdanau, R. Devon Hjelm, Alessandro Sordoni and Aaron C. Courville
NAACL 2021
(2021-05-07)
aclanthology.org[Also on arXiv preprint arXiv:2105.03519 (2021-05-07)]
Data-Efficient Reinforcement Learning with Self-Predictive Representations
Max Schwarzer, Ankesh Anand, Rishab Goel, R Devon Hjelm, Aaron Courville and Philip Bachman
ICLR 2021
(2021-05-03)
papyrus.bib.umontreal.ca PDF[Also on arXiv preprint arXiv:2007.05929 (2020-07-12)]

### 2021-03

Implicit Regularization via Neural Feature Alignment
Aristide Baratin, Thomas George, César Laurent, R Devon Hjelm, Guillaume Lajoie, Pascal Vincent and Simon Lacoste-Julien
AISTATS 2021
(2021-03-18)
proceedings.mlr.press PDF[Also on arXiv preprint arXiv:2008.00938 (2020-08-03)]
Pretraining Reward-Free Representations for Data-Efficient Reinforcement Learning
Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Charlin, R Devon Hjelm, Philip Bachman and Aaron Courville
ICLR 2021
(2021-03-09)
openreview.net PDF

### 2020-10

Zero-Shot Learning from scratch (ZFS): leveraging local compositional representations.
Tristan Sylvain, Linda Petrini and R. Devon Hjelm
arXiv preprint arXiv:2010.13320
(2020-10-22)
ui.adsabs.harvard.edu PDF
Cross-Modal Information Maximization for Medical Imaging: CMIM.
Tristan Sylvain, Francis Dutil, Tess Berthier, Lisa Di-Jorio, Margaux Luck, R. Devon Hjelm and Yoshua Bengio
arXiv preprint arXiv:2010.10593
(2020-10-20)
ui.adsabs.harvard.edu PDF

### 2020-08

Implicit Regularization in Deep Learning: A View from Function Space.
Aristide Baratin, Thomas George, César Laurent, R. Devon Hjelm, Guillaume Lajoie, Pascal Vincent and Simon Lacoste-Julien
(venue unknown)
(2020-08-03)
www.microsoft.com

### 2020-07

Data-Efficient Reinforcement Learning with Momentum Predictive Representations. (arXiv:2007.05929v2 [cs.LG] UPDATED)
Max Schwarzer, Ankesh Anand, Rishab Goel, R Devon Hjelm, Aaron Courville and Philip Bachman
arXiv Computer Science
(2020-07-24)
Data-Efficient Reinforcement Learning with Momentum Predictive Representations
Max Schwarzer, Ankesh Anand, Rishab Goel, R. Devon Hjelm, Aaron C. Courville and Philip Bachman
ICLR 2020
(2020-07-12)
www.microsoft.com PDF
An end-to-end approach for the verification problem: learning the right distance
Joao Monteiro, Isabela Albuquerque, Jahangir Alam, R Devon Hjelm and Tiago Falk
ICML 2020
(2020-07-12)
proceedings.mlr.press PDF[Also on arXiv preprint arXiv:2002.09469 (2020-02-21)]

### 2020-06

Deep Reinforcement and InfoMax Learning
Bogdan Mazoure, Remi Tachet des Combes, Thang Long Doan, Philip Bachman and R Devon Hjelm
NEURIPS 2020
(2020-06-12)
papers.nips.cc PDF[LATEST on arXiv preprint arXiv:2006.07217 (2020-06-12)]

### 2020-04

Locality and Compositionality in Zero-Shot Learning
Tristan Sylvain, Linda Petrini and Devon Hjelm
ICLR 2020
(2020-04-30)
iclr.cc PDF[Also on arXiv preprint arXiv:1912.12179 (2019-12-20)]

### 2020-03

Object-Centric Image Generation from Layouts
Tristan Sylvain, Pengchuan Zhang, Yoshua Bengio, R Devon Hjelm and Shikhar Sharma
AAAI 2020
(2020-03-16)
ojs.aaai.org[LATEST on arXiv preprint arXiv:2003.07449 (2020-03-16)]

### 2019-12

Unsupervised State Representation Learning in Atari
Ankesh Anand, Evan Racah, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre Côté and R Devon Hjelm
NEURIPS 2019
(2019-12-01)
papers.nips.cc PDF[Also on arXiv preprint arXiv:1906.08226 (2019-06-19)]

### 2019-10

Batch Weight for Domain Adaptation With Mass Shift
Mikolaj Binkowski, Devon Hjelm and Aaron Courville
ICCV 2019
(2019-10-01)
openaccess.thecvf.com PDF[Also on arXiv preprint arXiv:1905.12760 (2019-05-29)]
Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction
Alaaeldin El-Nouby, Shikhar Sharma, Hannes Schulz, R Devon Hjelm, Layla El Asri, Samira Ebrahimi Kahou, Yoshua Bengio and Graham Taylor
ICCV 2019
(2019-10-01)
openaccess.thecvf.com PDF[Also on arXiv preprint arXiv:1811.09845 (2018-11-24)]

### 2019-09

Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning
Thang Doan, Bogdan Mazoure, Audrey Durand, Joelle Pineau and R Devon Hjelm
arXiv: Learning
(2019-09-25)
ui.adsabs.harvard.edu PDF

### 2019-07

On-line Adaptative Curriculum Learning for GANs
Thang Doan, João Monteiro, Isabela Albuquerque, Bogdan Mazoure, Audrey Durand, Joelle Pineau and R. Devon Hjelm
AAAI 2019
(2019-07-17)
ojs.aaai.org PDF[Also on arXiv: Learning (2018-07-31)]

### 2019-06

Learning Representations by Maximizing Mutual Information Across Views
Philip Bachman, R Devon Hjelm and William Buchwalter
NEURIPS 2019
(2019-06-03)
papers.nips.cc PDF[Also on arXiv preprint arXiv:1906.00910 (2019-06-03)]

### 2019-05

Prediction of Progression to Alzheimer's disease with Deep InfoMax
Alex Fedorov, R Devon Hjelm, Anees Abrol, Zening Fu, Yuhui Du, Sergey Plis and Vince D. Calhoun
ICBHI 2019
(2019-05-19)
ieeexplore.ieee.org[Also on arXiv preprint arXiv:1904.10931 (2019-04-24)]
Leveraging exploration in off-policy algorithms via normalizing flows
Bogdan Mazoure, Thang Doan, Audrey Durand, R Devon Hjelm and Joelle Pineau
arXiv preprint arXiv:1905.06893
(2019-05-16)
ui.adsabs.harvard.edu PDF

### 2019-04

Spatio-Temporal Deep Graph Infomax.
Felix L. Opolka, Aaron Solomon, Catalina Cangea, Petar Velickovic, Pietro Liò and R. Devon Hjelm
arXiv preprint arXiv:1904.06316
(2019-04-12)
ui.adsabs.harvard.edu PDF

### 2019-03

Adversarial Mixup Resynthesizers
Christopher Beckham, Sina Honari, Alex Lamb, Vikas Verma, Farnoosh Ghadiri, R. Devon Hjelm and Christopher J. Pal
ICLR 2019
(2019-03-07)
dblp.uni-trier.de PDF[Also on None (2019-03-07)]

### 2019-01

Leveraging exploration in off-policy algorithms via normalizing flows.
Bogdan Mazoure, Thang Doan, Audrey Durand, Joelle Pineau and R. Devon Hjelm
Conference on Robot Learning
(2019-01-01)
proceedings.mlr.press PDF
On Adversarial Mixup Resynthesis
Christopher Beckham, Sina Honari, Vikas Verma, Alex M. Lamb, Farnoosh Ghadiri, R Devon Hjelm, Yoshua Bengio and Chris Pal
NEURIPS 2019
(2019-01-01)
papers.nips.cc PDF[LATEST on arXiv: Machine Learning (2019-03-07)]

### 2018-11

Keep Drawing It: Iterative language-based image generation and editing
Alaaeldin El-Nouby, Shikhar Sharma, Hannes Schulz, Devon Hjelm, Layla El Asri, Samira Ebrahimi Kahou, Yoshua Bengio and Graham W. Taylor
NEURIPS 2018
(2018-11-24)
[LATEST on None (2018-11-24)]

### 2018-09

Unsupervised one-to-many image translation
Samuel Lavoie-Marchildon, Sebastien Lachapelle, Mikołaj Bińkowski, Aaron Courville, Yoshua Bengio and R Devon Hjelm
(venue unknown)
(2018-09-27)
openreview.net PDF
Deep Graph Infomax
Petar Velickovic, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio and R. Devon Hjelm
ICLR 2018
(2018-09-27)
research.google PDF[Also on arXiv preprint arXiv:1809.10341 (2018-09-27)]

### 2018-08

Learning deep representations by mutual information estimation and maximization
R. Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Philip Bachman, Adam Trischler and Yoshua Bengio
ICLR 2018
(2018-08-20)
www.microsoft.com PDF[LATEST on arXiv: Machine Learning (2018-08-20)]

### 2018-07

Mutual Information Neural Estimation.
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville and Devon Hjelm
ICML 2018
(2018-07-03)
proceedings.mlr.press PDF

### 2018-06

Mine: mutual information neural estimation
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeswar, Sherjil Ozair, Yoshua Bengio, Aaron Courville and Devon Hjelm
ICML 2018
(2018-06-07)
www.microsoft.com

### 2018-02

Learning Generative Models with Locally Disentangled Latent Factors
Brady Neal, Alex Lamb, Sherjil Ozair, Devon Hjelm, Aaron Courville, Yoshua Bengio and Ioannis Mitliagkas
(venue unknown)
(2018-02-15)
openreview.net PDF
Boundary Seeking GANs
R Devon Hjelm, Athul Paul Jacob, Adam Trischler, Gerry Che, Kyunghyun Cho and Yoshua Bengio
ICLR 2018
(2018-02-15)
www.microsoft.com PDF
Variance Regularizing Adversarial Learning
Karan Grewal, R Devon Hjelm and Yoshua Bengio
arXiv preprint arXiv:1707.00309
(2018-02-15)
128.84.21.199 PDF

### 2018-01

MINE: Mutual Information Neural Estimation.
Ishmael Belghazi, Sai Rajeswar, Aristide Baratin, R. Devon Hjelm and Aaron C. Courville
arXiv preprint arXiv:1801.04062
(2018-01-12)
ui.adsabs.harvard.edu PDF

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