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R Devon Hjelm
R Devon Hjelm
Apple MLR, Mila
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Title
Cited by
Cited by
Year
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
21792018
Deep Graph Infomax.
P Velickovic, W Fedus, WL Hamilton, P Liò, Y Bengio, RD Hjelm
ICLR (Poster), 2019
1535*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
1441*2018
Learning representations by maximizing mutual information across views
P Bachman, RD Hjelm, W Buchwalter
Advances in neural information processing systems 32, 2019
11862019
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
6122014
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
2772017
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
2182019
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
2162015
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
1642014
Data-Efficient Reinforcement Learning with Self-Predictive Representations
M Schwarzer, A Anand, R Goel, RD Hjelm, A Courville, P Bachman
1612021
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
88*2019
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
752020
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
672021
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
662021
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
532019
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 …
SM Plis, MF Amin, A Chekroud, D Hjelm, E Damaraju, HJ Lee, JR Bustillo, ...
NeuroImage 181, 734-747, 2018
522018
Locality and compositionality in zero-shot learning
T Sylvain, L Petrini, D Hjelm
arXiv preprint arXiv:1912.12179, 2019
472019
Leveraging exploration in off-policy algorithms via normalizing flows
B Mazoure, T Doan, A Durand, J Pineau, RD Hjelm
Conference on Robot Learning, 430-444, 2020
442020
Iterative refinement of the approximate posterior for directed belief networks
D Hjelm, RR Salakhutdinov, K Cho, N Jojic, V Calhoun, J Chung
Advances in neural information processing systems 29, 2016
43*2016
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
402021
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Articles 1–20