R. Devon Hjelm

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 72

Top Topics

Iterative Refinement

Reinforcement Learning

Top Venues

Publications

Andrew Szot, Max Schwarzer, Harsh Agrawal, Bogdan Mazoure, Rin Metcalf, Walter Talbott, Natalie Mackraz, R. Devon Hjelm, Alexander T. Toshev
Large Language Models as Generalizable Policies for Embodied Tasks. ICLR (2024)
Amitis Shidani, R. Devon Hjelm, Jason Ramapuram, Russell Webb, Eeshan Gunesh Dhekane, Dan Busbridge
Poly-View Contrastive Learning. ICLR (2024)
Alex Fedorov, Eloy Geenjaar, Lei Wu, Tristan Sylvain, Thomas P. DeRamus, Margaux Luck, Maria B. Misiura, Girish Mittapalle, R. Devon Hjelm, Sergey M. Plis, Vince D. Calhoun
Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links. NeuroImage 285 (2024)
Andrew Szot, Bogdan Mazoure, Harsh Agrawal, R. Devon Hjelm, Zsolt Kira, Alexander Toshev
Grounding Multimodal Large Language Models in Actions. CoRR (2024)
Bogdan Mazoure, Walter Talbott, Miguel Ángel Bautista, R. Devon Hjelm, Alexander Toshev, Joshua M. Susskind
Value function estimation using conditional diffusion models for control. CoRR (2023)
Andrew Szot, Max Schwarzer, Harsh Agrawal, Bogdan Mazoure, Walter Talbott, Katherine Metcalf, Natalie Mackraz, R. Devon Hjelm, Alexander Toshev
Large Language Models as Generalizable Policies for Embodied Tasks. CoRR (2023)
Alex Fedorov, Eloy Geenjaar, Lei Wu, Tristan Sylvain, Thomas P. DeRamus, Margaux Luck, Maria B. Misiura, R. Devon Hjelm, Sergey M. Plis, Vince D. Calhoun
Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypes. CoRR (2022)
Ching-Yao Chuang, R. Devon Hjelm, Xin Wang, Vibhav Vineet, Neel Joshi, Antonio Torralba, Stefanie Jegelka, Yale Song
Robust Contrastive Learning against Noisy Views. CVPR (2022)
Gabriele Prato, Yale Song, Janarthanan Rajendran, R. Devon Hjelm, Neel Joshi, Sarath Chandar
PatchBlender: A Motion Prior for Video Transformers. CoRR (2022)
Ching-Yao Chuang, R. Devon Hjelm, Xin Wang, Vibhav Vineet, Neel Joshi, Antonio Torralba, Stefanie Jegelka, Yale Song
Robust Contrastive Learning against Noisy Views. CoRR (2022)
Bogdan Mazoure, Ahmed M Ahmed, R. Devon Hjelm, Andrey Kolobov, Patrick MacAlpine
Cross-Trajectory Representation Learning for Zero-Shot Generalization in RL. ICLR (2022)
Xu Ji, Razvan Pascanu, R. Devon Hjelm, Balaji Lakshminarayanan, Andrea Vedaldi
Test Sample Accuracy Scales with Training Sample Density in Neural Networks. CoLLAs (2022)
R. Devon Hjelm, Bogdan Mazoure, Florian Golemo, Felipe Frujeri, Mihai Jalobeanu, Andrey Kolobov
The Sandbox Environment for Generalizable Agent Research (SEGAR). CoRR (2022)
Xu Ji, Razvan Pascanu, R. Devon Hjelm, Andrea Vedaldi, Balaji Lakshminarayanan, Yoshua Bengio
Predicting Unreliable Predictions by Shattering a Neural Network. CoRR (2021)
Arian Hosseini, Siva Reddy, Dzmitry Bahdanau, R. Devon Hjelm, Alessandro Sordoni, Aaron C. Courville
Understanding by Understanding Not: Modeling Negation in Language Models. CoRR (2021)
Max Schwarzer, Ankesh Anand, Rishab Goel, R. Devon Hjelm, Aaron C. Courville, Philip Bachman
Data-Efficient Reinforcement Learning with Self-Predictive Representations. ICLR (2021)
Tristan Sylvain, Francis Dutil, Tess Berthier, Lisa Di-Jorio, Margaux Luck, R. Devon Hjelm, Yoshua Bengio
CMIM: Cross-Modal Information Maximization For Medical Imaging. ICASSP (2021)
Arian Hosseini, Siva Reddy, Dzmitry Bahdanau, R. Devon Hjelm, Alessandro Sordoni, Aaron C. Courville
Understanding by Understanding Not: Modeling Negation in Language Models. NAACL-HLT (2021)
Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Charlin, R. Devon Hjelm, Philip Bachman, Aaron C. Courville
Pretraining Representations for Data-Efficient Reinforcement Learning. CoRR (2021)
Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Charlin, R. Devon Hjelm, Philip Bachman, Aaron C. Courville
Pretraining Representations for Data-Efficient Reinforcement Learning. NeurIPS (2021)
Bogdan Mazoure, Ahmed M. Ahmed, Patrick MacAlpine, R. Devon Hjelm, Andrey Kolobov
Cross-Trajectory Representation Learning for Zero-Shot Generalization in RL. CoRR (2021)
Tristan Sylvain, Pengchuan Zhang, Yoshua Bengio, R. Devon Hjelm, Shikhar Sharma
Object-Centric Image Generation from Layouts. AAAI (2021)
Aristide Baratin, Thomas George, César Laurent, R. Devon Hjelm, Guillaume Lajoie, Pascal Vincent, Simon Lacoste-Julien
Implicit Regularization via Neural Feature Alignment. AISTATS (2021)
Aristide Baratin, Thomas George, César Laurent, R. Devon Hjelm, Guillaume Lajoie, Pascal Vincent, Simon Lacoste-Julien
Implicit Regularization in Deep Learning: A View from Function Space. CoRR (2020)
Tristan Sylvain, Linda Petrini, R. Devon Hjelm
Zero-Shot Learning from scratch (ZFS): leveraging local compositional representations. CoRR (2020)
João Monteiro, Isabela Albuquerque, Md. Jahangir Alam, R. Devon Hjelm, Tiago H. Falk
An end-to-end approach for the verification problem: learning the right distance. CoRR (2020)
Bogdan Mazoure, Remi Tachet des Combes, Thang Doan, Philip Bachman, R. Devon Hjelm
Deep Reinforcement and InfoMax Learning. NeurIPS (2020)
Tristan Sylvain, Pengchuan Zhang, Yoshua Bengio, R. Devon Hjelm, Shikhar Sharma
Object-Centric Image Generation from Layouts. CoRR (2020)
Max Schwarzer, Ankesh Anand, Rishab Goel, R. Devon Hjelm, Aaron C. Courville, Philip Bachman
Data-Efficient Reinforcement Learning with Momentum Predictive Representations. CoRR (2020)
R. Devon Hjelm, Philip Bachman
Representation Learning with Video Deep InfoMax. CoRR (2020)
Tristan Sylvain, Francis Dutil, Tess Berthier, Lisa Di-Jorio, Margaux Luck, R. Devon Hjelm, Yoshua Bengio
Cross-Modal Information Maximization for Medical Imaging: CMIM. CoRR (2020)
Bogdan Mazoure, Remi Tachet des Combes, Thang Doan, Philip Bachman, R. Devon Hjelm
Deep Reinforcement and InfoMax Learning. CoRR (2020)
Tristan Sylvain, Linda Petrini, R. Devon Hjelm
Locality and Compositionality in Zero-Shot Learning. ICLR (2020)
João Monteiro, Isabela Albuquerque, Md. Jahangir Alam, R. Devon Hjelm, Tiago H. Falk
An end-to-end approach for the verification problem: learning the right distance. ICML (2020)
Petar Velickovic, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R. Devon Hjelm
Deep Graph Infomax. ICLR (Poster) (2019)
R. Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Philip Bachman, Adam Trischler, Yoshua Bengio
Learning deep representations by mutual information estimation and maximization. ICLR (2019)
Philip Bachman, R. Devon Hjelm, William Buchwalter
Learning Representations by Maximizing Mutual Information Across Views. CoRR (2019)
Alex Fedorov, R. Devon Hjelm, Anees Abrol, Zening Fu, Yuhui Du, Sergey M. Plis, Vince D. Calhoun
Prediction of Progression to Alzheimer's disease with Deep InfoMax. BHI (2019)
Alex Fedorov, R. Devon Hjelm, Anees Abrol, Zening Fu, Yuhui Du, Sergey M. Plis, Vince D. Calhoun
Prediction of Progression to Alzheimer's disease with Deep InfoMax. CoRR (2019)
Bogdan Mazoure, Thang Doan, Audrey Durand, Joelle Pineau, R. Devon Hjelm
Leveraging exploration in off-policy algorithms via normalizing flows. CoRL (2019)
Bogdan Mazoure, Thang Doan, Audrey Durand, R. Devon Hjelm, Joelle Pineau
Leveraging exploration in off-policy algorithms via normalizing flows. CoRR (2019)
Ankesh Anand, Evan Racah, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre Côté, R. Devon Hjelm
Unsupervised State Representation Learning in Atari. CoRR (2019)
Christopher Beckham, Sina Honari, Vikas Verma, Alex Lamb, Farnoosh Ghadiri, R. Devon Hjelm, Yoshua Bengio, Chris Pal
On Adversarial Mixup Resynthesis. NeurIPS (2019)
Philip Bachman, R. Devon Hjelm, William Buchwalter
Learning Representations by Maximizing Mutual Information Across Views. NeurIPS (2019)
Mikolaj Binkowski, R. Devon Hjelm, Aaron C. Courville
Batch Weight for Domain Adaptation With Mass Shift. ICCV (2019)
Thang Doan, Bogdan Mazoure, Audrey Durand, Joelle Pineau, R. Devon Hjelm
Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning. CoRR (2019)
Alaaeldin El-Nouby, Shikhar Sharma, Hannes Schulz, R. Devon Hjelm, Layla El Asri, Samira Ebrahimi Kahou, Yoshua Bengio, Graham W. Taylor
Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction. ICCV (2019)
Felix L. Opolka, Aaron Solomon, Catalina Cangea, Petar Velickovic, Pietro Liò, R. Devon Hjelm
Spatio-Temporal Deep Graph Infomax. CoRR (2019)
Christopher Beckham, Sina Honari, Alex Lamb, Vikas Verma, Farnoosh Ghadiri, R. Devon Hjelm, Christopher J. Pal
Adversarial Mixup Resynthesizers. DGS@ICLR (2019)
Mikolaj Binkowski, R. Devon Hjelm, Aaron C. Courville
Batch weight for domain adaptation with mass shift. CoRR (2019)
Christopher Beckham, Sina Honari, Alex Lamb, Vikas Verma, Farnoosh Ghadiri, R. Devon Hjelm, Christopher J. Pal
Adversarial Mixup Resynthesizers. CoRR (2019)
Ankesh Anand, Evan Racah, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre Côté, R. Devon Hjelm
Unsupervised State Representation Learning in Atari. NeurIPS (2019)
Tristan Sylvain, Linda Petrini, R. Devon Hjelm
Locality and compositionality in zero-shot learning. CoRR (2019)
Thang Doan, João Monteiro, Isabela Albuquerque, Bogdan Mazoure, Audrey Durand, Joelle Pineau, R. Devon Hjelm
On-Line Adaptative Curriculum Learning for GANs. AAAI (2019)
Alaaeldin El-Nouby, Shikhar Sharma, Hannes Schulz, R. Devon Hjelm, Layla El Asri, Samira Ebrahimi Kahou, Yoshua Bengio, Graham W. Taylor
Keep Drawing It: Iterative language-based image generation and editing. CoRR (2018)
Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeswar, Sherjil Ozair, Yoshua Bengio, R. Devon Hjelm, Aaron C. Courville
Mutual Information Neural Estimation. ICML (2018)
Sergey M. Plis, Md Faijul Amin, Adam Chekroud, R. Devon Hjelm, Eswar Damaraju, Hyo Jong Lee, Juan R. Bustillo, KyungHyun Cho, Godfrey D. Pearlson, Vince D. Calhoun
Reading the (functional) writing on the (structural) wall: Multimodal fusion of brain structure and function via a deep neural network based translation approach reveals novel impairments in schizophrenia. NeuroImage 181 (2018)
Ishmael Belghazi, Sai Rajeswar, Aristide Baratin, R. Devon Hjelm, Aaron C. Courville
MINE: Mutual Information Neural Estimation. CoRR (2018)
Petar Velickovic, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R. Devon Hjelm
Deep Graph Infomax. CoRR (2018)
R. Devon Hjelm, Athul Paul Jacob, Adam Trischler, Gerry Che, Kyunghyun Cho, Yoshua Bengio
Boundary Seeking GANs. ICLR (Poster) (2018)
R. Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Adam Trischler, Yoshua Bengio
Learning deep representations by mutual information estimation and maximization. CoRR (2018)
Thang Doan, João Monteiro, Isabela Albuquerque, Bogdan Mazoure, Audrey Durand, Joelle Pineau, R. Devon Hjelm
Online Adaptative Curriculum Learning for GANs. CoRR (2018)
R. Devon Hjelm, Athul Paul Jacob, Tong Che, Kyunghyun Cho, Yoshua Bengio
Boundary-Seeking Generative Adversarial Networks. CoRR (2017)
Alex Lamb, R. Devon Hjelm, Yaroslav Ganin, Joseph Paul Cohen, Aaron C. Courville, Yoshua Bengio
GibbsNet: Iterative Adversarial Inference for Deep Graphical Models. CoRR (2017)
Tong Che, Yanran Li, Ruixiang Zhang, R. Devon Hjelm, Wenjie Li, Yangqiu Song, Yoshua Bengio
Maximum-Likelihood Augmented Discrete Generative Adversarial Networks. CoRR (2017)
Alex Lamb, R. Devon Hjelm, Yaroslav Ganin, Joseph Paul Cohen, Aaron C. Courville, Yoshua Bengio
GibbsNet: Iterative Adversarial Inference for Deep Graphical Models. NIPS (2017)
Vince D. Calhoun, Md Faijul Amin, R. Devon Hjelm, Eswar Damaraju, Sergey M. Plis
A deep-learning approach to translate between brain structure and functional connectivity. ICASSP (2017)
Anirudh Goyal, Nan Rosemary Ke, Alex Lamb, R. Devon Hjelm, Chris Pal, Joelle Pineau, Yoshua Bengio
ACtuAL: Actor-Critic Under Adversarial Learning. CoRR (2017)
Md Faijul Amin, Sergey M. Plis, Eswar Damaraju, R. Devon Hjelm, Kyunghyun Cho, Vince D. Calhoun
Multimodal fusion of brain structural and functional imaging with a deep neural machine translation approach. SSIAI (2016)
R. Devon Hjelm, Sergey M. Plis, Vince D. Calhoun
Recurrent Neural Networks for Spatiotemporal Dynamics of Intrinsic Networks from fMRI Data. CoRR (2016)
R. Devon Hjelm, Sergey M. Plis, Vince D. Calhoun
Variational Autoencoders for Feature Detection of Magnetic Resonance Imaging Data. CoRR (2016)
R. Devon Hjelm, Ruslan Salakhutdinov, Kyunghyun Cho, Nebojsa Jojic, Vince D. Calhoun, Junyoung Chung
Iterative Refinement of the Approximate Posterior for Directed Belief Networks. NIPS (2016)
Qingbao Yu, Erik B. Erhardt, Jing Sui, Yuhui Du, Hao He, R. Devon Hjelm, Mustafa S. Çetin, Srinivas Rachakonda, Robyn L. Miller, Godfrey D. Pearlson, Vince D. Calhoun
Assessing dynamic brain graphs of time-varying connectivity in fMRI data: Application to healthy controls and patients with schizophrenia. NeuroImage 107 (2015)
Eduardo Castro, R. Devon Hjelm, Sergey M. Plis, Laurent Dinh, Jessica A. Turner, Vince D. Calhoun
Deep independence network analysis of structural brain imaging: A simulation study. MLSP (2015)
R. Devon Hjelm, Kyunghyun Cho, Junyoung Chung, Russ R. Salakhutdinov, Vince D. Calhoun, Nebojsa Jojic
Iterative Refinement of Approximate Posterior for Training Directed Belief Networks. CoRR (2015)
R. Devon Hjelm, Vince D. Calhoun, Ruslan Salakhutdinov, Elena A. Allen, Tülay Adali, Sergey M. Plis
Restricted Boltzmann machines for neuroimaging: An application in identifying intrinsic networks. NeuroImage 96 (2014)
Sergey M. Plis, R. Devon Hjelm, Ruslan Salakhutdinov, Vince D. Calhoun
Deep learning for neuroimaging: a validation study. ICLR (Workshop Poster) (2014)