| Adversarially learned inference V Dumoulin, I Belghazi, B Poole, O Mastropietro, A Lamb, M Arjovsky, ... arXiv preprint arXiv:1606.00704, 2016 | 1104 | 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 | 892* | 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 | 380 | 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 | 315 | 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 | 308* | 2018 |
| 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 | 219 | 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 | 213 | 2019 |
| Variance reduction in sgd by distributed importance sampling G Alain, A Lamb, C Sankar, A Courville, Y Bengio arXiv preprint arXiv:1511.06481, 2015 | 108 | 2015 |
| Discriminative Regularization for Generative Models A Lamb, V Dumoulin, A Courville DeepVision Workshop (CVPR), 2016 | 76 | 2016 |
| Recurrent independent mechanisms A Goyal, A Lamb, J Hoffmann, S Sodhani, S Levine, Y Bengio, ... arXiv preprint arXiv:1909.10893, 2019 | 69 | 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 | 39* | 2019 |
| Graphmix: Regularized training of graph neural networks for semi-supervised learning V Verma, M Qu, A Lamb, Y Bengio, J Kannala, J Tang | 31* | 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 | 31* | 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 | 30* | 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 | 23* | 2020 |
| 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 | 16 | 2020 |
| Investigating twitter as a source for studying behavioral responses to epidemics A Lamb, MJ Paul, M Dredze 2012 AAAI Fall Symposium Series, 2012 | 16 | 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 | 11 | 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 | 9 | 2017 |
| Object files and schemata: Factorizing declarative and procedural knowledge in dynamical systems A Goyal, A Lamb, P Gampa, P Beaudoin, S Levine, C Blundell, Y Bengio, ... arXiv preprint arXiv:2006.16225, 2020 | 8* | 2020 |
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARVerified email at umontreal.ca
