| 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 | 1933 | 2018 |
| Mutual information neural estimation MI Belghazi, A Baratin, S Rajeshwar, S Ozair, Y Bengio, A Courville, ... International conference on machine learning, 531-540, 2018 | 1285* | 2018 |
| Deep Graph Infomax. P Velickovic, W Fedus, WL Hamilton, P Liò, Y Bengio, RD Hjelm ICLR (Poster), 2019 | 1280* | 2019 |
| Learning representations by maximizing mutual information across views P Bachman, RD Hjelm, W Buchwalter Advances in neural information processing systems 32, 2019 | 1017 | 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 | 583 | 2014 |
| 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 | 260 | 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 | 204 | 2015 |
| 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 | 196 | 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 | 159 | 2014 |
| Data-Efficient Reinforcement Learning with Self-Predictive Representations M Schwarzer, A Anand, R Goel, RD Hjelm, A Courville, P Bachman | 120 | 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 | 69* | 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 | 58 | 2020 |
| 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 | 49 | 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 32, 2019 | 48 | 2019 |
| 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 | 47 | 2021 |
| 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 | 46 | 2021 |
| Locality and compositionality in zero-shot learning T Sylvain, L Petrini, D Hjelm arXiv preprint arXiv:1912.12179, 2019 | 44 | 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 | 42 | 2020 |
| 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 | 41* | 2016 |
| 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 | 36 | 2019 |
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARVerified email at umontreal.ca
