| 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 | 3166 | 2018 |
| Deep reinforcement learning that matters P Henderson, R Islam, P Bachman, J Pineau, D Precup, D Meger Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 2527 | 2018 |
| Learning representations by maximizing mutual information across views P Bachman, RD Hjelm, W Buchwalter Advances in neural information processing systems 32, 2019 | 1687 | 2019 |
| Newsqa: A machine comprehension dataset A Trischler, T Wang, X Yuan, J Harris, A Sordoni, P Bachman, K Suleman arXiv preprint arXiv:1611.09830, 2016 | 969 | 2016 |
| Learning with pseudo-ensembles P Bachman, O Alsharif, D Precup Advances in neural information processing systems 27, 2014 | 707 | 2014 |
| Augmented cyclegan: Learning many-to-many mappings from unpaired data A Almahairi, S Rajeshwar, A Sordoni, P Bachman, A Courville International conference on machine learning, 195-204, 2018 | 542 | 2018 |
| Data-efficient reinforcement learning with self-predictive representations M Schwarzer, A Anand, R Goel, RD Hjelm, A Courville, P Bachman arXiv preprint arXiv:2007.05929, 2020 | 330 | 2020 |
| Machine comprehension by text-to-text neural question generation X Yuan, T Wang, C Gulcehre, A Sordoni, P Bachman, S Subramanian, ... arXiv preprint arXiv:1705.02012, 2017 | 222 | 2017 |
| Learning algorithms for active learning P Bachman, A Sordoni, A Trischler international conference on machine learning, 301-310, 2017 | 205 | 2017 |
| 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 | 154 | 2021 |
| Iterative alternating neural attention for machine reading A Sordoni, P Bachman, A Trischler, Y Bengio arXiv preprint arXiv:1606.02245, 2016 | 139 | 2016 |
| 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 | 118 | 2020 |
| Calibrating energy-based generative adversarial networks Z Dai, A Almahairi, P Bachman, E Hovy, A Courville arXiv preprint arXiv:1702.01691, 2017 | 114 | 2017 |
| Natural language comprehension with the epireader A Trischler, Z Ye, X Yuan, K Suleman arXiv preprint arXiv:1606.02270, 2016 | 107 | 2016 |
| Data generation as sequential decision making P Bachman, D Precup Advances in Neural Information Processing Systems 28, 2015 | 82 | 2015 |
| An architecture for deep, hierarchical generative models P Bachman Advances in Neural Information Processing Systems 29, 2016 | 59 | 2016 |
| Decomposed mutual information estimation for contrastive representation learning A Sordoni, N Dziri, H Schulz, G Gordon, P Bachman, RT Des Combes International Conference on Machine Learning, 9859-9869, 2021 | 33 | 2021 |
| Natural language generation in dialogue using lexicalized and delexicalized data S Sharma, J He, K Suleman, H Schulz, P Bachman arXiv preprint arXiv:1606.03632, 2016 | 33 | 2016 |
| Representation learning with video deep infomax RD Hjelm, P Bachman arXiv preprint arXiv:2007.13278, 2020 | 28* | 2020 |
| Parallel-hierarchical model for machine comprehension on small data A Trischler, Z Ye, X Yuan, P Bachman US Patent 10,691,999, 2020 | 22 | 2020 |
