| 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 | 273 | 2021 |
| Learning function from structure in neuromorphic networks LE Suárez, BA Richards, G Lajoie, B Misic Nature Machine Intelligence 3 (9), 771-786, 2021 | 91 | 2021 |
| Dimensionality compression and expansion in deep neural networks S Recanatesi, M Farrell, M Advani, T Moore, G Lajoie, E Shea-Brown arXiv preprint arXiv:1906.00443, 2019 | 78 | 2019 |
| Non-normal recurrent neural network (nnrnn): learning long time dependencies while improving expressivity with transient dynamics G Kerg, K Goyette, M Puelma Touzel, G Gidel, E Vorontsov, Y Bengio, ... Advances in neural information processing systems 32, 2019 | 76 | 2019 |
| 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 | 73 | 2020 |
| Predictive learning as a network mechanism for extracting low-dimensional latent space representations S Recanatesi, M Farrell, G Lajoie, S Deneve, M Rigotti, E Shea-Brown Nature communications 12 (1), 1417, 2021 | 65 | 2021 |
| Implicit regularization via neural feature alignment A Baratin, T George, C Laurent, RD Hjelm, G Lajoie, P Vincent, ... International Conference on Artificial Intelligence and Statistics, 2269-2277, 2021 | 56 | 2021 |
| Chaos and reliability in balanced spiking networks with temporal drive G Lajoie, KK Lin, E Shea-Brown Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 87 (5 …, 2013 | 54 | 2013 |
| Is a modular architecture enough? S Mittal, Y Bengio, G Lajoie Advances in Neural Information Processing Systems 35, 28747-28760, 2022 | 42 | 2022 |
| Systematic evaluation of causal discovery in visual model based reinforcement learning NR Ke, A Didolkar, S Mittal, A Goyal, G Lajoie, S Bauer, D Rezende, ... arXiv preprint arXiv:2107.00848, 2021 | 41 | 2021 |
| On lyapunov exponents for rnns: Understanding information propagation using dynamical systems tools R Vogt, M Puelma Touzel, E Shlizerman, G Lajoie Frontiers in Applied Mathematics and Statistics 8, 818799, 2022 | 38 | 2022 |
| Gradient-based learning drives robust representations in recurrent neural networks by balancing compression and expansion M Farrell, S Recanatesi, T Moore, G Lajoie, E Shea-Brown Nature Machine Intelligence 4 (6), 564-573, 2022 | 33 | 2022 |
| Multi-view manifold learning of human brain-state trajectories EL Busch, J Huang, A Benz, T Wallenstein, G Lajoie, G Wolf, ... Nature computational science 3 (3), 240-253, 2023 | 29 | 2023 |
| Reliability of cka as a similarity measure in deep learning MR Davari, S Horoi, A Natik, G Lajoie, G Wolf, E Belilovsky arXiv preprint arXiv:2210.16156, 2022 | 28 | 2022 |
| Untangling tradeoffs between recurrence and self-attention in artificial neural networks G Kerg, B Kanuparthi, AG ALIAS PARTH GOYAL, K Goyette, Y Bengio, ... Advances in Neural Information Processing Systems 33, 19443-19454, 2020 | 27 | 2020 |
| Driving reservoir models with oscillations: a solution to the extreme structural sensitivity of chaotic networks P Vincent-Lamarre, G Lajoie, JP Thivierge Journal of computational neuroscience 41, 305-322, 2016 | 27 | 2016 |
| A connectomics-based taxonomy of mammals LE Suarez, Y Yovel, MP van den Heuvel, O Sporns, Y Assaf, G Lajoie, ... Elife 11, e78635, 2022 | 25 | 2022 |
| Recurrent neural networks learn robust representations by dynamically balancing compression and expansion M Farrell, S Recanatesi, T Moore, G Lajoie, E Shea-Brown BioRxiv, 564476, 2019 | 22 | 2019 |
| Structured chaos shapes spike-response noise entropy in balanced neural networks G Lajoie, JP Thivierge, E Shea-Brown Frontiers in computational neuroscience 8, 123, 2014 | 22 | 2014 |
| Amortizing intractable inference in large language models EJ Hu, M Jain, E Elmoznino, Y Kaddar, G Lajoie, Y Bengio, N Malkin arXiv preprint arXiv:2310.04363, 2023 | 21 | 2023 |
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARumontreal.ca의 이메일 확인됨
