| Adversarially learned inference V Dumoulin, I Belghazi, B Poole, O Mastropietro, A Lamb, M Arjovsky, ... arXiv preprint arXiv:1606.00704, 2016 | 602 | 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 preprint arXiv:1605.02688, 2016 | 569 | 2016 |
| Separating fact from fear: Tracking flu infections on twitter A Lamb, MJ Paul, M Dredze Proceedings of the 2013 Conference of the North American Chapter of the …, 2013 | 235 | 2013 |
| Professor Forcing: A New Algorithm for Training Recurrent Networks A Goyal, A Lamb, Y Zhang, S Zhang, A Courville, Y Bengio Neural Information Processing Systems (NIPS) 2016, 2016 | 208* | 2016 |
| Discriminative Regularization for Generative Models A Lamb, V Dumoulin, A Courville DeepVision Workshop (CVPR), 2016 | 54 | 2016 |
| Variance reduction in sgd by distributed importance sampling G Alain, A Lamb, C Sankar, A Courville, Y Bengio arXiv preprint arXiv:1511.06481, 2015 | 38 | 2015 |
| Interpolation consistency training for semi-supervised learning V Verma, A Lamb, J Kannala, Y Bengio, D Lopez-Paz arXiv preprint arXiv:1903.03825, 2019 | 26 | 2019 |
| Manifold mixup: Encouraging meaningful on-manifold interpolation as a regularizer V Verma, A Lamb, C Beckham, A Courville, I Mitliagkis, Y Bengio stat 1050, 13, 2018 | 23 | 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 | 18 | 2018 |
| Harm de Vries, David Warde-Farley, Dustin J R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ... Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, and …, 2016 | 18 | 2016 |
| Manifold mixup: Better representations by interpolating hidden states V Verma, A Lamb, C Beckham, A Najafi, I Mitliagkas, A Courville, ... arXiv preprint arXiv:1806.05236, 2018 | 16 | 2018 |
| Fortified networks: Improving the robustness of deep networks by modeling the manifold of hidden representations A Lamb, J Binas, A Goyal, D Serdyuk, S Subramanian, I Mitliagkas, ... arXiv preprint arXiv:1804.02485, 2018 | 13 | 2018 |
| Manifold mixup: Learning better representations by interpolating hidden states V Verma, A Lamb, C Beckham, A Najafi, A Courville, I Mitliagkas, ... | 11 | 2018 |
| Investigating twitter as a source for studying behavioral responses to epidemics A Lamb, MJ Paul, M Dredze 2012 AAAI Fall Symposium Series, 2012 | 11 | 2012 |
| Theano: A Python framework for fast computation of mathematical expressions. arXiv e-prints abs/1605.02688 (May 2016) R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ... URL http://arxiv. org/abs/1605.02688, 2016 | 6 | 2016 |
| 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 | 4 | 2017 |
| GibbsNet: Iterative adversarial inference for deep graphical models AM Lamb, D Hjelm, Y Ganin, JP Cohen, AC Courville, Y Bengio Advances in Neural Information Processing Systems, 5089-5098, 2017 | 4 | 2017 |
| Adversarial Mixup Resynthesizers C Beckham, S Honari, A Lamb, V Verma, F Ghadiri, RD Hjelm, C Pal arXiv preprint arXiv:1903.02709, 2019 | 3 | 2019 |
| Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Accuracy A Lamb, V Verma, J Kannala, Y Bengio arXiv preprint arXiv:1906.06784, 2019 | 2 | 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 | 1 | 2019 |
Yoshua BengioProfessor, University of Montreal (Computer Sc. & Op. Res.), Mila, CIFAR, CRM, IVADO, REPARTI, GRSNCVerified email at mila.quebec
