R Devon Hjelm

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Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARVerified email at umontreal.ca
Aaron CourvilleProfessor, DIRO, Université de Montréal, Mila, Cifar CAI chairVerified email at umontreal.ca
Vince D. CalhounDirector-Translational Research in Neuroimaging and Data Science (TReNDS;GSU/GAtech/Emory)Verified email at gsu.edu
Petar VeličkovićSenior Staff Research Scientist, Google DeepMind | Affiliated Lecturer, University of CambridgeVerified email at google.com
Sergey PlisTReNDS center: GSU, Emory, and GATechVerified email at gsu.edu
Adam TrischlerMicrosoft Research, McGill UniversityVerified email at microsoft.com
Pietro LiòProfessor, University of CambridgeVerified email at cam.ac.uk
William FedusOpenAIVerified email at openai.com
Aristide BaratinResearch scientist, Samsung - SAIT AI Lab MontrealVerified email at mila.quebec
Sai RajeswarSr Researcher, ServiceNow Research, Université de Montréal, MilaVerified email at umontreal.ca
Alex FedorovEmory UniversityVerified email at emory.edu
Karan GrewalNumentaVerified email at numenta.com
Samuel LavoieMila, UdeMVerified email at mila.quebec
Bogdan MazoureApple MLR, MilaVerified email at apple.com
William L HamiltonAssistant Professor of Computer Science, McGill University and MilaVerified email at cs.mcgill.ca
Kyunghyun ChoNew York University, GenentechVerified email at nyu.edu
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUVerified email at cs.cmu.edu
Mohamed Ishmael BelghaziMontreal Institute for Learning AlgorithmsVerified email at mila.quebec
Ankesh AnandGoogle DeepMindVerified email at deepmind.com
Max SchwarzerOpenAIVerified email at openai.com

R Devon Hjelm

Apple MLR, Mila

Verified email at apple.com - Homepage

deep learning representation learning self-supervised learning generative models


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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	3283	2018
Deep Graph Infomax. P Velickovic, W Fedus, WL Hamilton, P Liò, Y Bengio, RD Hjelm ICLR (Poster), 2019	2970*	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	2266*	2018
Learning representations by maximizing mutual information across views P Bachman, RD Hjelm, W Buchwalter Advances in neural information processing systems 32, 2019	1746	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	702	2014
Data-Efficient Reinforcement Learning with Self-Predictive Representations M Schwarzer, A Anand, R Goel, RD Hjelm, A Courville, P Bachman	353	2021
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	320	2017
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	313	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	256	2015
Boundary-seeking generative adversarial networks RD Hjelm, AP Jacob, T Che, A Trischler, K Cho, Y Bengio arXiv preprint arXiv:1702.08431, 2017	233	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	183	2014
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	155	2021
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	154*	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	122	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	116	2021
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	92	2022
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	89	2021
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	78	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	74*	2021
Deep graph infomax V Petar, F William, L Hamilton William, L Pietro, B Yoshua, HR Devon ICLR (Poster) 2 (3), 4, 2019	72	2019

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