R. Devon Hjelm

85 papers A* 20A 1Misc 3Journal 49Unranked 12

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	The Design Space of Tri-Modal Masked Diffusion Models. CoRR Louis Béthune, Victor Turrisi, Bruno Kacper Mlodozeniec, Pau Rodríguez López, Lokesh Boominathan, Nikhil Bhendawade, Amitis Shidani, Joris Pelemans, Theo X. Olausson, R. Devon Hjelm, Paul Dixon, João Monteiro, Pierre Ablin, Vishnu Banna, Arno Blaas, Nick Henderson, Kari Noriy, Dan Busbridge, Joshua M. Susskind, Marco Cuturi, Irina Belousova, Luca Zappella, Russ Webb, Jason Ramapuram
2025	A*	`conf`	From Multimodal LLMs to Generalist Embodied Agents: Methods and Lessons. CVPR Andrew Szot, Bogdan Mazoure, Omar Attia, Aleksei Timofeev, Harsh Agrawal, R. Devon Hjelm, Zhe Gan, Zsolt Kira, Alexander Toshev
2025	J	`jnl`	GRACE: A Language Model Framework for Explainable Inverse Reinforcement Learning. CoRR Silvia Sapora, R. Devon Hjelm, Alexander Toshev, Omar Attia, Bogdan Mazoure
2025	A*	`conf`	On the Modeling Capabilities of Large Language Models for Sequential Decision Making. ICLR Martin Klissarov, R. Devon Hjelm, Alexander T. Toshev, Bogdan Mazoure
2024	J	`jnl`	From Multimodal LLMs to Generalist Embodied Agents: Methods and Lessons. CoRR Andrew Szot, Bogdan Mazoure, Omar Attia, Aleksei Timofeev, Harsh Agrawal, R. Devon Hjelm, Zhe Gan, Zsolt Kira, Alexander Toshev
2024	A*	`conf`	Grounding Multimodal Large Language Models in Actions. NeurIPS Andrew Szot, Bogdan Mazoure, Harsh Agrawal, R. Devon Hjelm, Zsolt Kira, Alexander Toshev
2024	J	`jnl`	Grounding Multimodal Large Language Models in Actions. CoRR Andrew Szot, Bogdan Mazoure, Harsh Agrawal, R. Devon Hjelm, Zsolt Kira, Alexander Toshev
2024	A*	`conf`	Large Language Models as Generalizable Policies for Embodied Tasks. ICLR Andrew Szot, Max Schwarzer, Harsh Agrawal, Bogdan Mazoure, Rin Metcalf, Walter Talbott, Natalie Mackraz, R. Devon Hjelm, Alexander T. Toshev
2024	J	`jnl`	On the Modeling Capabilities of Large Language Models for Sequential Decision Making. CoRR Martin Klissarov, R. Devon Hjelm, Alexander Toshev, Bogdan Mazoure
2024	A*	`conf`	Poly-View Contrastive Learning. ICLR Amitis Shidani, R. Devon Hjelm, Jason Ramapuram, Russell Webb, Eeshan Gunesh Dhekane, Dan Busbridge
2024	J	`jnl`	Poly-View Contrastive Learning. CoRR Amitis Shidani, R. Devon Hjelm, Jason Ramapuram, Russ Webb, Eeshan Gunesh Dhekane, Dan Busbridge
2024	J	`jnl`	Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links. NeuroImage 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
2023	J	`jnl`	Large Language Models as Generalizable Policies for Embodied Tasks. CoRR Andrew Szot, Max Schwarzer, Harsh Agrawal, Bogdan Mazoure, Walter Talbott, Katherine Metcalf, Natalie Mackraz, R. Devon Hjelm, Alexander Toshev
2023	J	`jnl`	Value function estimation using conditional diffusion models for control. CoRR Bogdan Mazoure, Walter Talbott, Miguel Ángel Bautista, R. Devon Hjelm, Alexander Toshev, Joshua M. Susskind
2022	A*	`conf`	Cross-Trajectory Representation Learning for Zero-Shot Generalization in RL. ICLR Bogdan Mazoure, Ahmed M. Ahmed, R. Devon Hjelm, Andrey Kolobov, Patrick MacAlpine
2022	J	`jnl`	PatchBlender: A Motion Prior for Video Transformers. CoRR Gabriele Prato, Yale Song, Janarthanan Rajendran, R. Devon Hjelm, Neel Joshi, Sarath Chandar
2022	A*	`conf`	Robust Contrastive Learning against Noisy Views. CVPR Ching-Yao Chuang, R. Devon Hjelm, Xin Wang, Vibhav Vineet, Neel Joshi, Antonio Torralba, Stefanie Jegelka, Yale Song
2022	J	`jnl`	Robust Contrastive Learning against Noisy Views. CoRR Ching-Yao Chuang, R. Devon Hjelm, Xin Wang, Vibhav Vineet, Neel Joshi, Antonio Torralba, Stefanie Jegelka, Yale Song
2022	J	`jnl`	Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypes. CoRR 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
2022	—	`conf`	Test Sample Accuracy Scales with Training Sample Density in Neural Networks. CoLLAs Xu Ji, Razvan Pascanu, R. Devon Hjelm, Balaji Lakshminarayanan, Andrea Vedaldi
2022	J	`jnl`	The Sandbox Environment for Generalizable Agent Research (SEGAR). CoRR R. Devon Hjelm, Bogdan Mazoure, Florian Golemo, Felipe Frujeri, Mihai Jalobeanu, Andrey Kolobov
2021	Misc	`conf`	CMIM: Cross-Modal Information Maximization For Medical Imaging. ICASSP Tristan Sylvain, Francis Dutil, Tess Berthier, Lisa Di-Jorio, Margaux Luck, R. Devon Hjelm, Yoshua Bengio
2021	J	`jnl`	Cross-Trajectory Representation Learning for Zero-Shot Generalization in RL. CoRR Bogdan Mazoure, Ahmed M. Ahmed, Patrick MacAlpine, R. Devon Hjelm, Andrey Kolobov
2021	A*	`conf`	Data-Efficient Reinforcement Learning with Self-Predictive Representations. ICLR Max Schwarzer, Ankesh Anand, Rishab Goel, R. Devon Hjelm, Aaron C. Courville, Philip Bachman
2021	A	`conf`	Implicit Regularization via Neural Feature Alignment. AISTATS Aristide Baratin, Thomas George, César Laurent, R. Devon Hjelm, Guillaume Lajoie, Pascal Vincent, Simon Lacoste-Julien
2021	A*	`conf`	Object-Centric Image Generation from Layouts. AAAI Tristan Sylvain, Pengchuan Zhang, Yoshua Bengio, R. Devon Hjelm, Shikhar Sharma
2021	J	`jnl`	Predicting Unreliable Predictions by Shattering a Neural Network. CoRR Xu Ji, Razvan Pascanu, R. Devon Hjelm, Andrea Vedaldi, Balaji Lakshminarayanan, Yoshua Bengio
2021	A*	`conf`	Pretraining Representations for Data-Efficient Reinforcement Learning. NeurIPS Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Charlin, R. Devon Hjelm, Philip Bachman, Aaron C. Courville
2021	J	`jnl`	Pretraining Representations for Data-Efficient Reinforcement Learning. CoRR Max Schwarzer, Nitarshan Rajkumar, Michael Noukhovitch, Ankesh Anand, Laurent Charlin, R. Devon Hjelm, Philip Bachman, Aaron C. Courville
2021	—	`conf`	Understanding by Understanding Not: Modeling Negation in Language Models. NAACL-HLT Arian Hosseini, Siva Reddy, Dzmitry Bahdanau, R. Devon Hjelm, Alessandro Sordoni, Aaron C. Courville
2021	J	`jnl`	Understanding by Understanding Not: Modeling Negation in Language Models. CoRR Arian Hosseini, Siva Reddy, Dzmitry Bahdanau, R. Devon Hjelm, Alessandro Sordoni, Aaron C. Courville
2020	A*	`conf`	An end-to-end approach for the verification problem: learning the right distance. ICML João Monteiro, Isabela Albuquerque, Jahangir Alam, R. Devon Hjelm, Tiago H. Falk
2020	J	`jnl`	An end-to-end approach for the verification problem: learning the right distance. CoRR João Monteiro, Isabela Albuquerque, Jahangir Alam, R. Devon Hjelm, Tiago H. Falk
2020	J	`jnl`	Cross-Modal Information Maximization for Medical Imaging: CMIM. CoRR Tristan Sylvain, Francis Dutil, Tess Berthier, Lisa Di-Jorio, Margaux Luck, R. Devon Hjelm, Yoshua Bengio
2020	J	`jnl`	Data-Efficient Reinforcement Learning with Momentum Predictive Representations. CoRR Max Schwarzer, Ankesh Anand, Rishab Goel, R. Devon Hjelm, Aaron C. Courville, Philip Bachman
2020	A*	`conf`	Deep Reinforcement and InfoMax Learning. NeurIPS Bogdan Mazoure, Remi Tachet des Combes, Thang Doan, Philip Bachman, R. Devon Hjelm
2020	J	`jnl`	Deep Reinforcement and InfoMax Learning. CoRR Bogdan Mazoure, Remi Tachet des Combes, Thang Doan, Philip Bachman, R. Devon Hjelm
2020	J	`jnl`	Implicit Regularization in Deep Learning: A View from Function Space. CoRR Aristide Baratin, Thomas George, César Laurent, R. Devon Hjelm, Guillaume Lajoie, Pascal Vincent, Simon Lacoste-Julien
2020	A*	`conf`	Locality and Compositionality in Zero-Shot Learning. ICLR Tristan Sylvain, Linda Petrini, R. Devon Hjelm
2020	J	`jnl`	Object-Centric Image Generation from Layouts. CoRR Tristan Sylvain, Pengchuan Zhang, Yoshua Bengio, R. Devon Hjelm, Shikhar Sharma
2020	J	`jnl`	Representation Learning with Video Deep InfoMax. CoRR R. Devon Hjelm, Philip Bachman
2020	J	`jnl`	Zero-Shot Learning from scratch (ZFS): leveraging local compositional representations. CoRR Tristan Sylvain, Linda Petrini, R. Devon Hjelm
2019	—	`conf`	Adversarial Mixup Resynthesizers. DGS@ICLR Christopher Beckham, Sina Honari, Alex Lamb, Vikas Verma, Farnoosh Ghadiri, R. Devon Hjelm, Christopher J. Pal
2019	J	`jnl`	Adversarial Mixup Resynthesizers. CoRR Christopher Beckham, Sina Honari, Alex Lamb, Vikas Verma, Farnoosh Ghadiri, R. Devon Hjelm, Christopher J. Pal
2019	J	`jnl`	Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning. CoRR Thang Doan, Bogdan Mazoure, Audrey Durand, Joelle Pineau, R. Devon Hjelm
2019	A*	`conf`	Batch Weight for Domain Adaptation With Mass Shift. ICCV Mikolaj Binkowski, R. Devon Hjelm, Aaron C. Courville
2019	J	`jnl`	Batch weight for domain adaptation with mass shift. CoRR Mikolaj Binkowski, R. Devon Hjelm, Aaron C. Courville
2019	—	`conf`	Deep Graph Infomax. ICLR (Poster) Petar Velickovic, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R. Devon Hjelm
2019	A*	`conf`	Learning Representations by Maximizing Mutual Information Across Views. NeurIPS Philip Bachman, R. Devon Hjelm, William Buchwalter
2019	J	`jnl`	Learning Representations by Maximizing Mutual Information Across Views. CoRR Philip Bachman, R. Devon Hjelm, William Buchwalter
2019	—	`conf`	Learning deep representations by mutual information estimation and maximization. ICLR R. Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Philip Bachman, Adam Trischler, Yoshua Bengio
2019	Misc	`conf`	Leveraging exploration in off-policy algorithms via normalizing flows. CoRL Bogdan Mazoure, Thang Doan, Audrey Durand, Joelle Pineau, R. Devon Hjelm
2019	J	`jnl`	Leveraging exploration in off-policy algorithms via normalizing flows. CoRR Bogdan Mazoure, Thang Doan, Audrey Durand, R. Devon Hjelm, Joelle Pineau
2019	J	`jnl`	Locality and compositionality in zero-shot learning. CoRR Tristan Sylvain, Linda Petrini, R. Devon Hjelm
2019	A*	`conf`	On Adversarial Mixup Resynthesis. NeurIPS Christopher Beckham, Sina Honari, Vikas Verma, Alex Lamb, Farnoosh Ghadiri, R. Devon Hjelm, Yoshua Bengio, Chris Pal
2019	A*	`conf`	On-Line Adaptative Curriculum Learning for GANs. AAAI Thang Doan, João Monteiro, Isabela Albuquerque, Bogdan Mazoure, Audrey Durand, Joelle Pineau, R. Devon Hjelm
2019	—	`conf`	Prediction of Progression to Alzheimer's disease with Deep InfoMax. BHI Alex Fedorov, R. Devon Hjelm, Anees Abrol, Zening Fu, Yuhui Du, Sergey M. Plis, Vince D. Calhoun
2019	J	`jnl`	Prediction of Progression to Alzheimer's disease with Deep InfoMax. CoRR Alex Fedorov, R. Devon Hjelm, Anees Abrol, Zening Fu, Yuhui Du, Sergey M. Plis, Vince D. Calhoun
2019	J	`jnl`	Spatio-Temporal Deep Graph Infomax. CoRR Felix L. Opolka, Aaron Solomon, Catalina Cangea, Petar Velickovic, Pietro Liò, R. Devon Hjelm
2019	A*	`conf`	Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction. ICCV Alaaeldin El-Nouby, Shikhar Sharma, Hannes Schulz, R. Devon Hjelm, Layla El Asri, Samira Ebrahimi Kahou, Yoshua Bengio, Graham W. Taylor
2019	A*	`conf`	Unsupervised State Representation Learning in Atari. NeurIPS Ankesh Anand, Evan Racah, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre Côté, R. Devon Hjelm
2019	J	`jnl`	Unsupervised State Representation Learning in Atari. CoRR Ankesh Anand, Evan Racah, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre Côté, R. Devon Hjelm
2018	—	`conf`	Boundary Seeking GANs. ICLR (Poster) R. Devon Hjelm, Athul Paul Jacob, Adam Trischler, Gerry Che, Kyunghyun Cho, Yoshua Bengio
2018	J	`jnl`	Deep Graph Infomax. CoRR Petar Velickovic, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, R. Devon Hjelm
2018	J	`jnl`	Keep Drawing It: Iterative language-based image generation and editing. CoRR Alaaeldin El-Nouby, Shikhar Sharma, Hannes Schulz, R. Devon Hjelm, Layla El Asri, Samira Ebrahimi Kahou, Yoshua Bengio, Graham W. Taylor
2018	J	`jnl`	Learning deep representations by mutual information estimation and maximization. CoRR R. Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Adam Trischler, Yoshua Bengio
2018	J	`jnl`	MINE: Mutual Information Neural Estimation. CoRR Ishmael Belghazi, Sai Rajeswar, Aristide Baratin, R. Devon Hjelm, Aaron C. Courville
2018	A*	`conf`	Mutual Information Neural Estimation. ICML Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeswar, Sherjil Ozair, Yoshua Bengio, R. Devon Hjelm, Aaron C. Courville
2018	J	`jnl`	Online Adaptative Curriculum Learning for GANs. CoRR Thang Doan, João Monteiro, Isabela Albuquerque, Bogdan Mazoure, Audrey Durand, Joelle Pineau, R. Devon Hjelm
2018	J	`jnl`	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 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
2017	Misc	`conf`	A deep-learning approach to translate between brain structure and functional connectivity. ICASSP Vince D. Calhoun, Md Faijul Amin, R. Devon Hjelm, Eswar Damaraju, Sergey M. Plis
2017	J	`jnl`	ACtuAL: Actor-Critic Under Adversarial Learning. CoRR Anirudh Goyal, Nan Rosemary Ke, Alex Lamb, R. Devon Hjelm, Chris Pal, Joelle Pineau, Yoshua Bengio
2017	J	`jnl`	Boundary-Seeking Generative Adversarial Networks. CoRR R. Devon Hjelm, Athul Paul Jacob, Tong Che, Kyunghyun Cho, Yoshua Bengio
2017	—	`conf`	GibbsNet: Iterative Adversarial Inference for Deep Graphical Models. NIPS Alex Lamb, R. Devon Hjelm, Yaroslav Ganin, Joseph Paul Cohen, Aaron C. Courville, Yoshua Bengio
2017	J	`jnl`	GibbsNet: Iterative Adversarial Inference for Deep Graphical Models. CoRR Alex Lamb, R. Devon Hjelm, Yaroslav Ganin, Joseph Paul Cohen, Aaron C. Courville, Yoshua Bengio
2017	J	`jnl`	Maximum-Likelihood Augmented Discrete Generative Adversarial Networks. CoRR Tong Che, Yanran Li, Ruixiang Zhang, R. Devon Hjelm, Wenjie Li, Yangqiu Song, Yoshua Bengio
2016	—	`conf`	Iterative Refinement of the Approximate Posterior for Directed Belief Networks. NIPS R. Devon Hjelm, Russ Salakhutdinov, Kyunghyun Cho, Nebojsa Jojic, Vince D. Calhoun, Junyoung Chung
2016	—	`conf`	Multimodal fusion of brain structural and functional imaging with a deep neural machine translation approach. SSIAI Md Faijul Amin, Sergey M. Plis, Eswar Damaraju, R. Devon Hjelm, Kyunghyun Cho, Vince D. Calhoun
2016	J	`jnl`	Recurrent Neural Networks for Spatiotemporal Dynamics of Intrinsic Networks from fMRI Data. CoRR R. Devon Hjelm, Sergey M. Plis, Vince D. Calhoun
2016	J	`jnl`	Variational Autoencoders for Feature Detection of Magnetic Resonance Imaging Data. CoRR R. Devon Hjelm, Sergey M. Plis, Vince D. Calhoun
2015	J	`jnl`	Assessing dynamic brain graphs of time-varying connectivity in fMRI data: Application to healthy controls and patients with schizophrenia. NeuroImage 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
2015	—	`conf`	Deep independence network analysis of structural brain imaging: A simulation study. MLSP Eduardo Castro, R. Devon Hjelm, Sergey M. Plis, Laurent Dinh, Jessica A. Turner, Vince D. Calhoun
2015	J	`jnl`	Iterative Refinement of Approximate Posterior for Training Directed Belief Networks. CoRR R. Devon Hjelm, Kyunghyun Cho, Junyoung Chung, Ruslan Salakhutdinov, Vince D. Calhoun, Nebojsa Jojic
2014	—	`conf`	Deep learning for neuroimaging: a validation study. ICLR (Workshop Poster) Sergey M. Plis, R. Devon Hjelm, Ruslan Salakhutdinov, Vince D. Calhoun
2014	J	`jnl`	Restricted Boltzmann machines for neuroimaging: An application in identifying intrinsic networks. NeuroImage R. Devon Hjelm, Vince D. Calhoun, Ruslan Salakhutdinov, Elena A. Allen, Tülay Adali, Sergey M. Plis