R Devon Hjelm - Google Scholar

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	All	Since 2015
Citations	1841	1821
h-index	14	14
i10-index	17	17

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201420152016201720182019202013 34 89 146 296 678 572

Co-authors

Yoshua BengioProfessor, University of Montreal (Computer Sc. & Op. Res.), Mila, CIFAR, CRM, IVADO, REPARTI, GRSNCVerified email at mila.quebec
Vince D. CalhounDirector-Translational Research in Neuroimaging and Data Science (TReNDS;GSU/GAtech/Emory)Verified email at gsu.edu
Sergey PlisTReNDS center: GSU, Emory, and GATechVerified email at gsu.edu
Aaron CourvilleUniversité de MontréalVerified email at umontreal.ca
Kyunghyun ChoNew York UniversityVerified email at nyu.edu
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUVerified email at cs.cmu.edu
Adam TrischlerMicrosoft Research, McGill UniversityVerified email at microsoft.com
Philip BachmanMicrosoft ResearchVerified email at microsoft.com
Jessica TurnerProfessor of Psychology and Neuroscience, Georgia State UniversityVerified email at gsu.edu
Karan GrewalUniversity of TorontoVerified email at cs.toronto.edu
Samuel LavoieMila, UdeMVerified email at mila.quebec
Elena AllenRodin ScientificVerified email at rodinscientific.com
Aristide BaratinMila, Université de MontréalVerified email at umontreal.ca
Mohamed Ishmael BelghaziMontreal Institute for Learning AlgorithmsVerified email at mila.quebec
Alex FedorovGeorgia Institute of TechnologyVerified email at gatech.edu
Petar VeličkovićResearch Scientist, DeepMindVerified email at google.com
P. Lió, Pietro Lio, P. Lio, Pietro Liò, Pie...University of CambridgeVerified email at cam.ac.uk
William L HamiltonAssistant Professor of Computer Science, McGill University and MilaVerified email at cs.mcgill.ca
William FedusPhD Student, Montreal Institute for Learning AlgorithmsVerified email at umontreal.ca
Jeffrey D LongProfessor of Psychiatry, Carver College of Medicine, University of IowaVerified email at uiowa.edu

R Devon Hjelm

Microsoft Research, University of Montreal, Mila

Verified email at microsoft.com - Homepage

deep learning representation learning self-supervised learning generative models


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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	351	2014
Mutual information neural estimation MI Belghazi, A Baratin, S Rajeshwar, S Ozair, Y Bengio, A Courville, ... International Conference on Machine Learning, 531-540, 2018	307*	2018
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	280	2018
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	136	2017
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	133	2015
Deep Graph Infomax. P Velickovic, W Fedus, WL Hamilton, P Liò, Y Bengio, RD Hjelm ICLR (Poster), 2019	109	2019
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	105	2014
Learning representations by maximizing mutual information across views P Bachman, RD Hjelm, W Buchwalter Advances in Neural Information Processing Systems, 15535-15545, 2019	97	2019
Boundary-seeking generative adversarial networks RD Hjelm, AP Jacob, T Che, A Trischler, K Cho, Y Bengio arXiv preprint arXiv:1702.08431, 2017	97	2017
Deep graph infomax P Veličković, W Fedus, WL Hamilton, P Liò, Y Bengio, RD Hjelm arXiv preprint arXiv:1809.10341, 2018	46	2018
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	23*	2016
Unsupervised state representation learning in atari A Anand, E Racah, S Ozair, Y Bengio, MA Côté, RD Hjelm Advances in Neural Information Processing Systems, 8769-8782, 2019	19	2019
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	17	2016
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	15*	2018
On adversarial mixup resynthesis C Beckham, S Honari, V Verma, AM Lamb, F Ghadiri, RD Hjelm, Y Bengio, ... Advances in neural information processing systems, 4346-4357, 2019	14*	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 novel … SM Plis, MF Amin, A Chekroud, D Hjelm, E Damaraju, HJ Lee, JR Bustillo, ... Neuroimage 181, 734-747, 2018	13	2018
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	12*	2019
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	9	2020
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, 3470-3477, 2019	7	2019
Actual: Actor-critic under adversarial learning A Goyal, NR Ke, A Lamb, RD Hjelm, C Pal, J Pineau, Y Bengio arXiv preprint arXiv:1711.04755, 2017	7	2017

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