R Devon Hjelm
R Devon Hjelm
Apple MLR, Mila
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Cited by
Cited by
Learning deep representations by mutual information estimation and maximization
RD Hjelm, A Fedorov, S Lavoie-Marchildon, K Grewal, P Bachman, ...
arXiv preprint arXiv:1808.06670, 2018
Deep Graph Infomax.
P Velickovic, W Fedus, WL Hamilton, P Liò, Y Bengio, RD Hjelm
ICLR (Poster), 2019
Mutual information neural estimation
MI Belghazi, A Baratin, S Rajeshwar, S Ozair, Y Bengio, A Courville, ...
International conference on machine learning, 531-540, 2018
Learning representations by maximizing mutual information across views
P Bachman, RD Hjelm, W Buchwalter
Advances in neural information processing systems 32, 2019
Deep learning for neuroimaging: a validation study
SM Plis, DR Hjelm, R Salakhutdinov, EA Allen, HJ Bockholt, JD Long, ...
Frontiers in neuroscience 8, 229, 2014
Maximum-likelihood augmented discrete generative adversarial networks
T Che, Y Li, R Zhang, RD Hjelm, W Li, Y Song, Y Bengio
arXiv preprint arXiv:1702.07983, 2017
Data-Efficient Reinforcement Learning with Self-Predictive Representations
M Schwarzer, A Anand, R Goel, RD Hjelm, A Courville, P Bachman
Unsupervised state representation learning in atari
A Anand, E Racah, S Ozair, Y Bengio, MA Côté, RD Hjelm
Advances in neural information processing systems 32, 2019
Assessing dynamic brain graphs of time-varying connectivity in fMRI data: application to healthy controls and patients with schizophrenia
Q Yu, EB Erhardt, J Sui, Y Du, H He, D Hjelm, MS Cetin, S Rachakonda, ...
Neuroimage 107, 345-355, 2015
Boundary-seeking generative adversarial networks
RD Hjelm, AP Jacob, T Che, A Trischler, K Cho, Y Bengio
arXiv preprint arXiv:1702.08431, 2017
Restricted Boltzmann Machines for Neuroimaging: an Application in Identifying Intrinsic Networks
D Hjelm, V Calhoun, EA Allen, T Adali, R Salakhutdinov, SM Plis
NeuroImage, in Press, 2014
Tell, draw, and repeat: Generating and modifying images based on continual linguistic instruction
A El-Nouby, S Sharma, H Schulz, D Hjelm, LE Asri, SE Kahou, Y Bengio, ...
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019
Pretraining representations for data-efficient reinforcement learning
M Schwarzer, N Rajkumar, M Noukhovitch, A Anand, L Charlin, RD Hjelm, ...
Advances in Neural Information Processing Systems 34, 12686-12699, 2021
Deep reinforcement and infomax learning
B Mazoure, R Tachet des Combes, TL Doan, P Bachman, RD Hjelm
Advances in Neural Information Processing Systems 33, 3686-3698, 2020
Object-centric image generation from layouts
T Sylvain, P Zhang, Y Bengio, RD Hjelm, S Sharma
Proceedings of the AAAI Conference on Artificial Intelligence 35 (3), 2647-2655, 2021
Understanding by understanding not: Modeling negation in language models
A Hosseini, S Reddy, D Bahdanau, RD Hjelm, A Sordoni, A Courville
arXiv preprint arXiv:2105.03519, 2021
Deep graph infomax
V Petar, F William, L Hamilton William, L Pietro, B Yoshua, HR Devon
ICLR (Poster) 2 (3), 4, 2019
Robust contrastive learning against noisy views
CY Chuang, RD Hjelm, X Wang, V Vineet, N Joshi, A Torralba, S Jegelka, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
On adversarial mixup resynthesis
C Beckham, S Honari, V Verma, AM Lamb, F Ghadiri, RD Hjelm, Y Bengio, ...
Advances in neural information processing systems 32, 2019
Implicit regularization via neural feature alignment
A Baratin, T George, C Laurent, RD Hjelm, G Lajoie, P Vincent, ...
International Conference on Artificial Intelligence and Statistics, 2269-2277, 2021
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