Omar Chehab
Omar Chehab
Postdoctoral Researcher, CREST
Verified email at - Homepage
Cited by
Cited by
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
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
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
The optimal noise in noise-contrastive learning is not what you think
O Chehab, A Gramfort, A Hyvärinen
Uncertainty in Artificial Intelligence, 307-316, 2022
Learning with self-supervision on EEG data
A Gramfort, H Banville, O Chehab, A Hyvärinen, D Engemann
2021 9th International Winter Conference on Brain-Computer Interface (BCI), 1-2, 2021
Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond
O Chehab, A Hyvarinen, A Risteski
arXiv preprint arXiv:2310.03902, 2023
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
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