O. Chehab

Deep Recurrent Encoder: an end-to-end network to model magnetoencephalography at scale

Alexandre Défossez, J. King, Jean-Christophe Loiseau, 2021, Neurons, Behavior, Data analysis, and Theory.

The Optimal Noise in Noise-Contrastive Learning Is Not What You Think

Alexandre Gramfort, O. Chehab, Aapo Hyvärinen, 2022, UAI.

Deep Recurrent Encoder: A scalable end-to-end network to model brain signals

Alexandre Défossez, J. King, Jean-Christophe Loiseau, 2021, ArXiv.

A mean-field approach to the dynamics of networks of complex neurons, from nonlinear Integrate-and-Fire to Hodgkin–Huxley models

A. Destexhe, M. Carlu, O. Chehab, 2019, bioRxiv.

Uncovering the structure of clinical EEG signals with self-supervised learning

Aapo Hyvärinen, Alexandre Gramfort, Hubert J. Banville, 2020, Journal of neural engineering.

Optimizing the Noise in Self-Supervised Learning: from Importance Sampling to Noise-Contrastive Estimation

A. Hyvarinen, Alexandre Gramfort, O. Chehab, 2023, ArXiv.

Deep Recurrent Encoder: A scalable end-to-end network to model brain signals

Alexandre Défossez, O. Chehab, Jean-Christophe Loiseau, 2021, ArXiv.