Devon Hjelm
Devon Hjelm
Microsoft Research, University of Montreal, Mila
Verified email at microsoft.com - Homepage
TitleCited byYear
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
2762014
Mine: mutual information neural estimation
MI Belghazi, A Baratin, S Rajeswar, S Ozair, Y Bengio, A Courville, ...
arXiv preprint arXiv:1801.04062, 2018
1472018
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
1142015
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
952017
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
902014
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
882018
Boundary-seeking generative adversarial networks
RD Hjelm, AP Jacob, T Che, A Trischler, K Cho, Y Bengio
arXiv preprint arXiv:1702.08431, 2017
732017
Deep graph infomax
P Veličković, W Fedus, WL Hamilton, P Liò, Y Bengio, RD Hjelm
arXiv preprint arXiv:1809.10341, 2018
422018
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
18*2016
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
172016
Spatio-Temporal Dynamics of Intrinsic Networks in Functional Magnetic Imaging Data Using Recurrent Neural Networks
RD Hjelm, E Damaraju, K Cho, H Laufs, SM Plis, VD Calhoun
Frontiers in neuroscience 12, 600, 2018
11*2018
Deep learning for neuroimaging: a validation study. Front. Neurosci. 8: 229
SM Plis, DR Hjelm, R Salakhutdinov, EA Allen, HJ Bockholt, JD Long, ...
102014
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
72018
Learning representations by maximizing mutual information across views
P Bachman, RD Hjelm, W Buchwalter
arXiv preprint arXiv:1906.00910, 2019
62019
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 International Conference on Computer Vision, 10304-10312, 2019
6*2019
On-line Adaptative Curriculum Learning for GANs
T Doan, J Monteiro, I Albuquerque, B Mazoure, A Durand, J Pineau, ...
42019
Variance regularizing adversarial learning
K Grewal, RD Hjelm, Y Bengio
arXiv preprint arXiv:1707.00309, 2017
42017
GibbsNet: Iterative adversarial inference for deep graphical models
AM Lamb, D Hjelm, Y Ganin, JP Cohen, AC Courville, Y Bengio
Advances in Neural Information Processing Systems, 5089-5098, 2017
42017
Variational autoencoders for feature detection of magnetic resonance imaging data
RD Hjelm, SM Plis, VC Calhoun
arXiv preprint arXiv:1603.06624, 2016
42016
Adversarial Mixup Resynthesizers
C Beckham, S Honari, A Lamb, V Verma, F Ghadiri, RD Hjelm, C Pal
arXiv preprint arXiv:1903.02709, 2019
32019
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