Deep learning for neuroimaging: a validation study. - Sergey M. Plis; R. Devon Hjelm; Ruslan Salakhutdinov; Vince D. Calhoun (ICLR (Workshop Poster), 2014)

Restricted Boltzmann machines for neuroimaging: An application in identifying intrinsic networks. - R. Devon Hjelm; Vince D. Calhoun; Ruslan Salakhutdinov; Elena A. Allen; Tülay Adali; Sergey M. Plis (NeuroImage, 2014)

Deep independence network analysis of structural brain imaging: A simulation study. - Eduardo Castro; R. Devon Hjelm; Sergey M. Plis; Laurent Dinh; Jessica A. Turner; Vince D. Calhoun (MLSP, 2015)

Iterative Refinement of Approximate Posterior for Training Directed Belief Networks. - R. Devon Hjelm; Kyunghyun Cho; Junyoung Chung; Ruslan Salakhutdinov; Vince D. Calhoun; Nebojsa Jojic (CoRR, 2015)

Assessing dynamic brain graphs of time-varying connectivity in fMRI data: Application to healthy controls and patients with schizophrenia. - Qingbao Yu; Erik B. Erhardt; Jing Sui; Yuhui Du; Hao He 002; R. Devon Hjelm; Mustafa S. Çetin; Srinivas Rachakonda; Robyn L. Miller; Godfrey D. Pearlson; Vince D. Calhoun (NeuroImage, 2015)

Iterative Refinement of the Approximate Posterior for Directed Belief Networks. - R. Devon Hjelm; Russ Salakhutdinov; Kyunghyun Cho; Nebojsa Jojic; Vince D. Calhoun; Junyoung Chung (NIPS, 2016)

Multimodal fusion of brain structural and functional imaging with a deep neural machine translation approach. - Md Faijul Amin; Sergey M. Plis; Eswar Damaraju; R. Devon Hjelm; Kyunghyun Cho; Vince D. Calhoun (SSIAI, 2016)

Variational Autoencoders for Feature Detection of Magnetic Resonance Imaging Data. - R. Devon Hjelm; Sergey M. Plis; Vince D. Calhoun (CoRR, 2016)

Recurrent Neural Networks for Spatiotemporal Dynamics of Intrinsic Networks from fMRI Data. - R. Devon Hjelm; Sergey M. Plis; Vince D. Calhoun (CoRR, 2016)

A deep-learning approach to translate between brain structure and functional connectivity. - Vince D. Calhoun; Md Faijul Amin; R. Devon Hjelm; Eswar Damaraju; Sergey M. Plis (ICASSP, 2017)

GibbsNet: Iterative Adversarial Inference for Deep Graphical Models. - Alex Lamb; R. Devon Hjelm; Yaroslav Ganin; Joseph Paul Cohen; Aaron C. Courville; Yoshua Bengio (NIPS, 2017)

Maximum-Likelihood Augmented Discrete Generative Adversarial Networks. - Tong Che; Yanran Li; Ruixiang Zhang; R. Devon Hjelm; Wenjie Li 002; Yangqiu Song; Yoshua Bengio (CoRR, 2017)

Boundary-Seeking Generative Adversarial Networks. - R. Devon Hjelm; Athul Paul Jacob; Tong Che; Kyunghyun Cho; Yoshua Bengio (CoRR, 2017)

ACtuAL: Actor-Critic Under Adversarial Learning. - Anirudh Goyal; Nan Rosemary Ke; Alex Lamb; R. Devon Hjelm; Chris Pal; Joelle Pineau; Yoshua Bengio (CoRR, 2017)

Boundary Seeking GANs. - R. Devon Hjelm; Athul Paul Jacob; Adam Trischler; Gerry Che; Kyunghyun Cho; Yoshua Bengio (ICLR (Poster), 2018)

Mutual Information Neural Estimation. - Mohamed Ishmael Belghazi; Aristide Baratin; Sai Rajeswar; Sherjil Ozair; Yoshua Bengio; R. Devon Hjelm; Aaron C. Courville (ICML, 2018)

MINE: Mutual Information Neural Estimation. - Ishmael Belghazi; Sai Rajeswar; Aristide Baratin; R. Devon Hjelm; Aaron C. Courville (CoRR, 2018)

Online Adaptative Curriculum Learning for GANs. - Thang Doan; João Monteiro 002; Isabela Albuquerque; Bogdan Mazoure; Audrey Durand; Joelle Pineau; R. Devon Hjelm (CoRR, 2018)

Learning deep representations by mutual information estimation and maximization. - R. Devon Hjelm; Alex Fedorov; Samuel Lavoie-Marchildon; Karan Grewal; Adam Trischler; Yoshua Bengio (CoRR, 2018)

Keep Drawing It: Iterative language-based image generation and editing. - Alaaeldin El-Nouby; Shikhar Sharma 001; Hannes Schulz; R. Devon Hjelm; Layla El Asri; Samira Ebrahimi Kahou; Yoshua Bengio; Graham W. Taylor (CoRR, 2018)

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. - 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 (NeuroImage, 2018)

On-Line Adaptative Curriculum Learning for GANs. - Thang Doan; João Monteiro 002; Isabela Albuquerque; Bogdan Mazoure; Audrey Durand; Joelle Pineau; R. Devon Hjelm (AAAI, 2019)

Prediction of Progression to Alzheimer's disease with Deep InfoMax. - Alex Fedorov; R. Devon Hjelm; Anees Abrol; Zening Fu; Yuhui Du; Sergey M. Plis; Vince D. Calhoun (BHI, 2019)

Leveraging exploration in off-policy algorithms via normalizing flows. - Bogdan Mazoure; Thang Doan; Audrey Durand; Joelle Pineau; R. Devon Hjelm (CoRL, 2019)

Batch Weight for Domain Adaptation With Mass Shift. - Mikolaj Binkowski; R. Devon Hjelm; Aaron C. Courville (ICCV, 2019)

Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic Instruction. - Alaaeldin El-Nouby; Shikhar Sharma 001; Hannes Schulz; R. Devon Hjelm; Layla El Asri; Samira Ebrahimi Kahou; Yoshua Bengio; Graham W. Taylor (ICCV, 2019)

Adversarial Mixup Resynthesizers. - Christopher Beckham; Sina Honari; Alex Lamb; Vikas Verma; Farnoosh Ghadiri; R. Devon Hjelm; Christopher J. Pal (DGS@ICLR, 2019)

Learning deep representations by mutual information estimation and maximization. - R. Devon Hjelm; Alex Fedorov; Samuel Lavoie-Marchildon; Karan Grewal; Philip Bachman; Adam Trischler; Yoshua Bengio (ICLR, 2019)

Deep Graph Infomax. - Petar Velickovic; William Fedus; William L. Hamilton; Pietro Liò; Yoshua Bengio; R. Devon Hjelm (ICLR (Poster), 2019)

Unsupervised State Representation Learning in Atari. - Ankesh Anand; Evan Racah; Sherjil Ozair; Yoshua Bengio; Marc-Alexandre Côté; R. Devon Hjelm (NeurIPS, 2019)

Learning Representations by Maximizing Mutual Information Across Views. - Philip Bachman; R. Devon Hjelm; William Buchwalter (NeurIPS, 2019)

On Adversarial Mixup Resynthesis. - Christopher Beckham; Sina Honari; Vikas Verma; Alex Lamb; Farnoosh Ghadiri; R. Devon Hjelm; Yoshua Bengio; Chris Pal (NeurIPS, 2019)

Spatio-Temporal Deep Graph Infomax. - Felix L. Opolka; Aaron Solomon; Catalina Cangea; Petar Velickovic; Pietro Liò; R. Devon Hjelm (CoRR, 2019)

Attraction-Repulsion Actor-Critic for Continuous Control Reinforcement Learning. - Thang Doan; Bogdan Mazoure; Audrey Durand; Joelle Pineau; R. Devon Hjelm (CoRR, 2019)

Locality and Compositionality in Zero-Shot Learning. - Tristan Sylvain; Linda Petrini; R. Devon Hjelm (ICLR, 2020)

An end-to-end approach for the verification problem: learning the right distance. - João Monteiro 002; Isabela Albuquerque; Jahangir Alam 001; R. Devon Hjelm; Tiago H. Falk (ICML, 2020)

Deep Reinforcement and InfoMax Learning. - Bogdan Mazoure; Remi Tachet des Combes; Thang Doan; Philip Bachman; R. Devon Hjelm (NeurIPS, 2020)

Data-Efficient Reinforcement Learning with Momentum Predictive Representations. - Max Schwarzer; Ankesh Anand; Rishab Goel; R. Devon Hjelm; Aaron C. Courville; Philip Bachman (CoRR, 2020)

Representation Learning with Video Deep InfoMax. - R. Devon Hjelm; Philip Bachman (CoRR, 2020)

Implicit Regularization in Deep Learning: A View from Function Space. - Aristide Baratin; Thomas George; César Laurent; R. Devon Hjelm; Guillaume Lajoie; Pascal Vincent; Simon Lacoste-Julien (CoRR, 2020)

Cross-Modal Information Maximization for Medical Imaging: CMIM. - Tristan Sylvain; Francis Dutil; Tess Berthier; Lisa Di-Jorio; Margaux Luck; R. Devon Hjelm; Yoshua Bengio (CoRR, 2020)

Zero-Shot Learning from scratch (ZFS): leveraging local compositional representations. - Tristan Sylvain; Linda Petrini; R. Devon Hjelm (CoRR, 2020)

Object-Centric Image Generation from Layouts. - Tristan Sylvain; Pengchuan Zhang; Yoshua Bengio; R. Devon Hjelm; Shikhar Sharma 001 (AAAI, 2021)

Implicit Regularization via Neural Feature Alignment. - Aristide Baratin; Thomas George; César Laurent; R. Devon Hjelm; Guillaume Lajoie; Pascal Vincent; Simon Lacoste-Julien (AISTATS, 2021)

CMIM: Cross-Modal Information Maximization For Medical Imaging. - Tristan Sylvain; Francis Dutil; Tess Berthier; Lisa Di-Jorio; Margaux Luck; R. Devon Hjelm; Yoshua Bengio (ICASSP, 2021)

Data-Efficient Reinforcement Learning with Self-Predictive Representations. - Max Schwarzer; Ankesh Anand; Rishab Goel; R. Devon Hjelm; Aaron C. Courville; Philip Bachman (ICLR, 2021)

Understanding by Understanding Not: Modeling Negation in Language Models. - Arian Hosseini; Siva Reddy; Dzmitry Bahdanau; R. Devon Hjelm; Alessandro Sordoni; Aaron C. Courville (NAACL-HLT, 2021)

Pretraining Representations for Data-Efficient Reinforcement Learning. - Max Schwarzer; Nitarshan Rajkumar; Michael Noukhovitch; Ankesh Anand; Laurent Charlin; R. Devon Hjelm; Philip Bachman; Aaron C. Courville (NeurIPS, 2021)

Predicting Unreliable Predictions by Shattering a Neural Network. - Xu Ji; Razvan Pascanu; R. Devon Hjelm; Andrea Vedaldi; Balaji Lakshminarayanan; Yoshua Bengio (CoRR, 2021)

Test Sample Accuracy Scales with Training Sample Density in Neural Networks. - Xu Ji; Razvan Pascanu; R. Devon Hjelm; Balaji Lakshminarayanan; Andrea Vedaldi (CoLLAs, 2022)

Robust Contrastive Learning against Noisy Views. - Ching-Yao Chuang; R. Devon Hjelm; Xin Wang 066; Vibhav Vineet; Neel Joshi; Antonio Torralba 001; Stefanie Jegelka; Yale Song (CVPR, 2022)

Cross-Trajectory Representation Learning for Zero-Shot Generalization in RL. - Bogdan Mazoure; Ahmed M. Ahmed 004; R. Devon Hjelm; Andrey Kolobov; Patrick MacAlpine (ICLR, 2022)

The Sandbox Environment for Generalizable Agent Research (SEGAR). - R. Devon Hjelm; Bogdan Mazoure; Florian Golemo; Felipe Frujeri; Mihai Jalobeanu; Andrey Kolobov (CoRR, 2022)

Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypes. - Alex Fedorov; Eloy Geenjaar; Lei Wu 013; Tristan Sylvain; Thomas P. DeRamus; Margaux Luck; Maria B. Misiura; R. Devon Hjelm; Sergey M. Plis; Vince D. Calhoun (CoRR, 2022)

PatchBlender: A Motion Prior for Video Transformers. - Gabriele Prato 001; Yale Song; Janarthanan Rajendran; R. Devon Hjelm; Neel Joshi; Sarath Chandar (CoRR, 2022)

Value function estimation using conditional diffusion models for control. - Bogdan Mazoure; Walter Talbott; Miguel Ángel Bautista 001; R. Devon Hjelm; Alexander Toshev; Joshua M. Susskind (CoRR, 2023)

Large Language Models as Generalizable Policies for Embodied Tasks. - Andrew Szot; Max Schwarzer; Harsh Agrawal; Bogdan Mazoure; Walter Talbott; Katherine Metcalf; Natalie Mackraz; R. Devon Hjelm; Alexander Toshev (CoRR, 2023)

Poly-View Contrastive Learning. - Amitis Shidani; R. Devon Hjelm; Jason Ramapuram; Russell Webb; Eeshan Gunesh Dhekane; Dan Busbridge (ICLR, 2024)

Large Language Models as Generalizable Policies for Embodied Tasks. - Andrew Szot; Max Schwarzer; Harsh Agrawal; Bogdan Mazoure; Rin Metcalf; Walter Talbott; Natalie Mackraz; R. Devon Hjelm; Alexander T. Toshev (ICLR, 2024)

Grounding Multimodal Large Language Models in Actions. - Andrew Szot; Bogdan Mazoure; Harsh Agrawal; R. Devon Hjelm; Zsolt Kira; Alexander Toshev (NeurIPS, 2024)

Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links. - Alex Fedorov; Eloy Geenjaar; Lei Wu 013; Tristan Sylvain; Thomas P. DeRamus; Margaux Luck; Maria B. Misiura; Girish Mittapalle; R. Devon Hjelm; Sergey M. Plis; Vince D. Calhoun (NeuroImage, 2024)

From Multimodal LLMs to Generalist Embodied Agents: Methods and Lessons. - Andrew Szot; Bogdan Mazoure; Omar Attia; Aleksei Timofeev; Harsh Agrawal; R. Devon Hjelm; Zhe Gan; Zsolt Kira; Alexander Toshev (CVPR, 2025)

On the Modeling Capabilities of Large Language Models for Sequential Decision Making. - Martin Klissarov; R. Devon Hjelm; Alexander T. Toshev; Bogdan Mazoure (ICLR, 2025)