| On the impact of the activation function on deep neural networks training S Hayou, A Doucet, J Rousseau 36th International Conference on Machine Learning (ICML 2019), 2019 | 286 | 2019 |
| On the selection of initialization and activation function for deep neural networks S Hayou, A Doucet, J Rousseau arXiv preprint arXiv:1805.08266, 2018 | 97 | 2018 |
| Stable ResNet S Hayou, E Clerico, B He, G Deligiannidis, A Doucet, J Rousseau 24th International Conference on Artificial Intelligence and Statistics …, 2021 | 64 | 2021 |
| LoRA+: Efficient Low Rank Adaptation of Large Models S Hayou, N Ghosh, B Yu ICML 2024 (arXiv:2402.12354), 2024 | 55 | 2024 |
| Efficient low rank adaptation of large models S Hayou, N Ghosh, BY LoRA arXiv preprint arXiv:2402.12354 1, 2024 | 54 | 2024 |
| Robust Pruning at Initialization S Hayou, JF Ton, A Doucet, YW Teh International Conference on Learning Representations (ICLR 2021), 2021 | 54 | 2021 |
| Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks G Yang, D Yu, C Zhu, S Hayou ICLR 2024, 2023 | 33 | 2023 |
| Mean-field Behaviour of Neural Tangent Kernel for Deep Neural Networks S Hayou, A Doucet, J Rousseau arXiv preprint arXiv:1905.13654, 2019 | 30 | 2019 |
| On the infinite-depth limit of finite-width neural networks S Hayou Transactions on Machine Learning Research (arXiv:2210.00688), 2022 | 25 | 2022 |
| Feature learning and signal propagation in deep neural networks Y Lou, CE Mingard, S Hayou International Conference on Machine Learning, 14248-14282, 2022 | 17 | 2022 |
| On the impact of the activation function on deep neural networks training 2019 S Hayou, A Doucet, J Rousseau arXiv preprint arXiv:1902.06853, 1902 | 17* | 1902 |
| Width and Depth Limits Commute in Residual Networks S Hayou, G Yang ICML 2023 (arXiv preprint arXiv:2302.00453), 2023 | 16 | 2023 |
| Pruning untrained neural networks: Principles and analysis S Hayou, JF Ton, A Doucet, Y Whye Teh arXiv e-prints, arXiv: 2002.08797, 2020 | 15 | 2020 |
| How Bad is Training on Synthetic Data? A Statistical Analysis of Language Model Collapse MEA Seddik, SW Chen, S Hayou, P Youssef, M Debbah arXiv preprint arXiv:2404.05090, 2024 | 14 | 2024 |
| Training dynamics of deep networks using stochastic gradient descent via neural tangent kernel S Hayou, A Doucet, J Rousseau arXiv preprint arXiv:1905.13654, 2019 | 12 | 2019 |
| Leave-one-out distinguishability in machine learning J Ye, A Borovykh, S Hayou, R Shokri ICLR 2024, arXiv preprint arXiv:2309.17310, 2023 | 10 | 2023 |
| Regularization in ResNet with Stochastic Depth S Hayou, F Ayed NeurIPS 2021, arXiv:2106.03091, 2021 | 10 | 2021 |
| Feature Learning in Infinite Depth Neural Networks G Yang, D Yu, C Zhu, S Hayou The Twelfth International Conference on Learning Representations, 2023 | 9 | 2023 |
| The curse of depth in kernel regime S Hayou, A Doucet, J Rousseau I (Still) Can't Believe It's Not Better! Workshop at NeurIPS 2021, 41-47, 2022 | 8 | 2022 |
| Data pruning and neural scaling laws: fundamental limitations of score-based algorithms F Ayed, S Hayou Transactions on Machine Learning Research 2023. (arXiv preprint arXiv:2302 …, 2023 | 7 | 2023 |
Arnaud DoucetGoogle DeepMind在 google.com 的电子邮件经过验证
