| Theano: A Python framework for fast computation of mathematical expressions R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ... arXiv, arXiv: 1605.02688, 2016 | 989 | 2016 |
| Towards end-to-end speech recognition with deep convolutional neural networks Y Zhang, M Pezeshki, P Brakel, S Zhang, CLY Bengio, A Courville arXiv preprint arXiv:1701.02720, 2017 | 498 | 2017 |
| Zoneout: Regularizing rnns by randomly preserving hidden activations D Krueger, T Maharaj, J Kramár, M Pezeshki, N Ballas, NR Ke, A Goyal, ... arXiv preprint arXiv:1606.01305, 2016 | 387 | 2016 |
| Gradient starvation: A learning proclivity in neural networks M Pezeshki, O Kaba, Y Bengio, AC Courville, D Precup, G Lajoie Advances in Neural Information Processing Systems 34, 1256-1272, 2021 | 292 | 2021 |
| Theano: A Python framework for fast computation of mathematical expressions TTD Team, R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, ... arXiv preprint arXiv:1605.02688, 2016 | 219 | 2016 |
| Negative momentum for improved game dynamics G Gidel, RA Hemmat, M Pezeshki, R Le Priol, G Huang, S Lacoste-Julien, ... The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 207 | 2019 |
| Simple data balancing achieves competitive worst-group-accuracy BY Idrissi, M Arjovsky, M Pezeshki, D Lopez-Paz Conference on Causal Learning and Reasoning, 336-351, 2022 | 168 | 2022 |
| Deconstructing the Ladder Network Architecture M Pezeshki, L Fan, P Brakel, A Courville, Y Bengio arXiv preprint arXiv:1511.06430, 2015 | 138 | 2015 |
| Theano: A Python framework for fast computation of mathematical expressions. arXiv R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ... arXiv preprint arXiv:1605.02688 10, 2016 | 51 | 2016 |
| On the learning dynamics of deep neural networks R Tachet, M Pezeshki, S Shabanian, A Courville, Y Bengio arXiv preprint arXiv:1809.06848, 2018 | 43* | 2018 |
| Multi-scale Feature Learning Dynamics: Insights for Double Descent M Pezeshki, A Mitra, Y Bengio, G Lajoie https://arxiv.org/pdf/2112.03215.pdf, 2021 | 24 | 2021 |
| Comparison three methods of clustering: K-means, spectral clustering and hierarchical clustering K Kowsari, T Borsche, A Ulbig, G Andersson, AM Saxe, JL McClelland, ... arXiv Preprint, 2013 | 16* | 2013 |
| Sequence modeling using gated recurrent neural networks M Pezeshki arXiv preprint arXiv:1501.00299, 2015 | 15 | 2015 |
| Discovering environments with XRM M Pezeshki, D Bouchacourt, M Ibrahim, N Ballas, P Vincent, D Lopez-Paz arXiv preprint arXiv:2309.16748, 2023 | 12 | 2023 |
| Predicting grokking long before it happens: A look into the loss landscape of models which grok P Notsawo Jr, H Zhou, M Pezeshki, I Rish, G Dumas arXiv preprint arXiv:2306.13253, 2023 | 12 | 2023 |
| Deep belief networks for image denoising MA Keyvanrad, M Pezeshki, MA Homayounpour arXiv preprint arXiv:1312.6158, 2013 | 12 | 2013 |
| Feedback-guided Data Synthesis for Imbalanced Classification R Askari Hemmat, M Pezeshki, F Bordes, M Drozdzal, A Romero-Soriano arXiv e-prints, arXiv: 2310.00158, 2023 | 8* | 2023 |
| Compositional Risk Minimization D Mahajan, M Pezeshki, I Mitliagkas, K Ahuja, P Vincent arXiv preprint arXiv:2410.06303, 2024 | | 2024 |
| Dynamics of learning and generalization in neural networks M Pezeshki | | 2022 |
| Deliberate Practice with Synthetic Data R Askari-Hemmat, M Pezeshki, P Astolfi, M Hall, F Bordes, J Verbeek, ... Adaptive Foundation Models: Evolving AI for Personalized and Efficient Learning, 0 | | |
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARVerified email at umontreal.ca
