Alex Lamb
Alex Lamb
Microsoft Research (NYC), Université de Montréal, Google Brain, Amazon, Twitch PhD Fellow
Verified email at - Homepage
Cited by
Cited by
Adversarially learned inference
V Dumoulin, I Belghazi, B Poole, O Mastropietro, A Lamb, M Arjovsky, ...
arXiv preprint arXiv:1606.00704, 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
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
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
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
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
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
Variance reduction in sgd by distributed importance sampling
G Alain, A Lamb, C Sankar, A Courville, Y Bengio
arXiv preprint arXiv:1511.06481, 2015
Recurrent independent mechanisms
A Goyal, A Lamb, J Hoffmann, S Sodhani, S Levine, Y Bengio, ...
arXiv preprint arXiv:1909.10893, 2019
Discriminative Regularization for Generative Models
A Lamb, V Dumoulin, A Courville
DeepVision Workshop (CVPR), 2016
Graphmix: Regularized training of graph neural networks for semi-supervised learning
V Verma, M Qu, A Lamb, Y Bengio, J Kannala, J Tang
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
On adversarial mixup resynthesis
C Beckham, S Honari, V Verma, A Lamb, F Ghadiri, RD Hjelm, Y Bengio, ...
arXiv preprint arXiv:1903.02709, 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
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
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
Coordination among neural modules through a shared global workspace
A Goyal, A Didolkar, A Lamb, K Badola, NR Ke, N Rahaman, J Binas, ...
arXiv preprint arXiv:2103.01197, 2021
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
Investigating twitter as a source for studying behavioral responses to epidemics
A Lamb, MJ Paul, M Dredze
2012 AAAI Fall Symposium Series, 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
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