| Adversarially learned inference V Dumoulin, I Belghazi, B Poole, O Mastropietro, A Lamb, M Arjovsky, ... arXiv preprint arXiv:1606.00704, 2016 | 988 | 2016 |
| Theano: A Python framework for fast computation of mathematical expressions R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ... arXiv e-prints, arXiv: 1605.02688, 2016 | 849* | 2016 |
| Professor forcing: A new algorithm for training recurrent networks A Lamb, A Goyal, Y Zhang, S Zhang, AC Courville, Y Bengio Advances in neural information processing systems 29, 4601-4609, 2016 | 335 | 2016 |
| Separating fact from fear: Tracking flu infections on twitter A Lamb, M Paul, M Dredze Proceedings of the 2013 Conference of the North American Chapter of the …, 2013 | 295 | 2013 |
| Manifold mixup: Better representations by interpolating hidden states A Lamb, V Verma, C Beckham, A Najafi, I Mitliagkas, A Courville, ... International Conference on Machine Learning (ICML) 2019, and arXiv preprint …, 2018 | 223* | 2018 |
| Interpolation consistency training for semi-supervised learning V Verma, A Lamb, J Kannala, Y Bengio, D Lopez-Paz International Joint Conference on Artificial Intelligence (IJCAI) 2019, and …, 2019 | 153 | 2019 |
| Deep learning for classical japanese literature T Clanuwat, M Bober-Irizar, A Kitamoto, A Lamb, K Yamamoto, D Ha arXiv preprint arXiv:1812.01718, 2018 | 153 | 2018 |
| Variance reduction in sgd by distributed importance sampling G Alain, A Lamb, C Sankar, A Courville, Y Bengio arXiv preprint arXiv:1511.06481, 2015 | 87 | 2015 |
| Discriminative Regularization for Generative Models A Lamb, V Dumoulin, A Courville DeepVision Workshop (CVPR), 2016 | 69 | 2016 |
| Recurrent independent mechanisms A Goyal, A Lamb, J Hoffmann, S Sodhani, S Levine, Y Bengio, ... arXiv preprint arXiv:1909.10893, 2019 | 42* | 2019 |
| State-reification networks: Improving generalization by modeling the distribution of hidden representations A Lamb, J Binas, A Goyal, S Subramanian, I Mitliagkas, D Kazakov, ... International Conference on Machine Learning (ICML) 2019, 2019 | 35* | 2019 |
| Interpolated adversarial training: Achieving robust neural networks without sacrificing too much accuracy A Lamb, V Verma, J Kannala, Y Bengio Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security …, 2019 | 22* | 2019 |
| On adversarial mixup resynthesis C Beckham, S Honari, V Verma, A Lamb, F Ghadiri, RD Hjelm, Y Bengio, ... arXiv preprint arXiv:1903.02709, 2019 | 21* | 2019 |
| Graphmix: Regularized training of graph neural networks for semi-supervised learning V Verma, M Qu, A Lamb, Y Bengio, J Kannala, J Tang arXiv preprint arXiv:1909.11715, 2019 | 19 | 2019 |
| KuroNet: Regularized Residual U-Nets for End-to-End Kuzushiji Character Recognition A Lamb, T Clanuwat, A Kitamoto Springer Nature Computer Science and ICDAR 2019 (Oral), 2020 | 18* | 2020 |
| Investigating Twitter as a Source for Studying Behavioral Responses to Epidemics. A Lamb, MJ Paul, M Dredze AAAI Fall Symposium: Information Retrieval and Knowledge Discovery in …, 2012 | 15 | 2012 |
| Gibbsnet: Iterative adversarial inference for deep graphical models A Lamb, D Hjelm, Y Ganin, JP Cohen, A Courville, Y Bengio arXiv preprint arXiv:1712.04120, 2017 | 9 | 2017 |
| 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 | 8 | 2017 |
| Learning to combine top-down and bottom-up signals in recurrent neural networks with attention over modules S Mittal, A Lamb, A Goyal, V Voleti, M Shanahan, G Lajoie, M Mozer, ... International Conference on Machine Learning, 6972-6986, 2020 | 5 | 2020 |
| End-to-end pre-modern Japanese character (kuzushiji) spotting with deep learning C Tarin, L Alex, K Asanobu じんもんこん 2018 論文集, 15-20, 2018 | 5 | 2018 |
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARVerified email at umontreal.ca
