R Devon Hjelm
R Devon Hjelm
Microsoft Research, University of Montreal, Mila
Verified email at microsoft.com - Homepage
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
9302018
Mutual information neural estimation
MI Belghazi, A Baratin, S Rajeshwar, S Ozair, Y Bengio, A Courville, ...
International Conference on Machine Learning, 531-540, 2018
730*2018
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
6012014
Learning representations by maximizing mutual information across views
P Bachman, RD Hjelm, W Buchwalter
arXiv preprint arXiv:1906.00910, 2019
4702019
Deep Graph Infomax.
P Velickovic, W Fedus, WL Hamilton, P Li˛, Y Bengio, RD Hjelm
ICLR (Poster), 2019
3932019
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
1932017
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
1692015
Boundary-seeking generative adversarial networks
RD Hjelm, AP Jacob, T Che, A Trischler, K Cho, Y Bengio
arXiv preprint arXiv:1702.08431, 2017
1442017
Deep graph infomax
P Veličković, W Fedus, WL Hamilton, P Li˛, Y Bengio, RD Hjelm
arXiv preprint arXiv:1809.10341, 2018
1412018
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
1312014
Unsupervised state representation learning in atari
A Anand, E Racah, S Ozair, Y Bengio, MA C˘tÚ, RD Hjelm
arXiv preprint arXiv:1906.08226, 2019
902019
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, 4691-4699, 2016
37*2016
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 Visioná…, 2019
33*2019
Reading the (functional) writing on the (structural) wall: Multimodal fusion of brain structure and function via a deep neural network based translation approach reveals novelá…
SM Plis, MF Amin, A Chekroud, D Hjelm, E Damaraju, HJ Lee, JR Bustillo, ...
NeuroImage 181, 734-747, 2018
322018
On adversarial mixup resynthesis
C Beckham, S Honari, V Verma, A Lamb, F Ghadiri, RD Hjelm, Y Bengio, ...
arXiv preprint arXiv:1903.02709, 2019
31*2019
Data-Efficient Reinforcement Learning with Self-Predictive Representations
M Schwarzer, A Anand, R Goel, RD Hjelm, A Courville, P Bachman
29*2021
Deep independence network analysis of structural brain imaging: application to schizophrenia
E Castro, RD Hjelm, SM Plis, L Dinh, JA Turner, VD Calhoun
IEEE transactions on medical imaging 35 (7), 1729-1740, 2016
27*2016
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
242020
On-line adaptative curriculum learning for gans
T Doan, J Monteiro, I Albuquerque, B Mazoure, A Durand, J Pineau, ...
Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 3470-3477, 2019
232019
Locality and compositionality in zero-shot learning
T Sylvain, L Petrini, D Hjelm
arXiv preprint arXiv:1912.12179, 2019
222019
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