| Uncovering the structure of clinical EEG signals with self-supervised learning H Banville, O Chehab, A Hyvärinen, DA Engemann, A Gramfort Journal of Neural Engineering 18 (4), 046020, 2021 | 190 | 2021 |
| A mean-field approach to the dynamics of networks of complex neurons, from nonlinear Integrate-and-Fire to Hodgkin–Huxley models M Carlu, O Chehab, L Dalla Porta, D Depannemaecker, C Héricé, ... Journal of neurophysiology 123 (3), 1042-1051, 2020 | 40 | 2020 |
| The optimal noise in noise-contrastive learning is not what you think O Chehab, A Gramfort, A Hyvärinen Uncertainty in Artificial Intelligence (UAI), 307-316, 2022 | 14 | 2022 |
| Deep Recurrent Encoder: an end-to-end network to model magnetoencephalography at scale O Chehab, A Défossez, L Jean-Christophe, A Gramfort, JR King Neurons, Behavior, Data Analysis, and Theory, 2022 | 13* | 2022 |
| Learning with self-supervision on EEG data A Gramfort, H Banville, O Chehab, A Hyvärinen, D Engemann International Winter Conference on Brain-Computer Interface (BCI), 1-2, 2021 | 7 | 2021 |
| Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond O Chehab, A Hyvarinen, A Risteski Spotlight, Advances in Neural Information Processing Systems (NeurIPS) 36, 2024 | 1 | 2024 |
| Optimizing the Noise in Self-Supervised Learning: from Importance Sampling to Noise-Contrastive Estimation O Chehab, A Gramfort, A Hyvarinen arXiv preprint arXiv:2301.09696, 2023 | 1 | 2023 |
| A Practical Diffusion Path for Sampling O Chehab, A Korba International Conference on Machine Learning (ICML), Workshop on Structured …, 2024 | | 2024 |
| Advances in Self-Supervised Learning: applications to neuroscience and sample-efficiency O Chehab Inria, Université Paris-Saclay, 2023 | | 2023 |
Alexandre GramfortMetaVerified email at meta.com
