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2020 – today
- 2024
[i7]Omar Chehab, Anna Korba:
A Practical Diffusion Path for Sampling. CoRR abs/2406.14040 (2024)- [i6]Omar Chehab, Anna Korba, Austin Stromme, Adrien Vacher:
Provable Convergence and Limitations of Geometric Tempering for Langevin Dynamics. CoRR abs/2410.09697 (2024) - 2023
- [c3]Omar Chehab, Aapo Hyvärinen, Andrej Risteski:
Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond. NeurIPS 2023 - [i5]Omar Chehab
, Alexandre Gramfort, Aapo Hyvärinen:
Optimizing the Noise in Self-Supervised Learning: from Importance Sampling to Noise-Contrastive Estimation. CoRR abs/2301.09696 (2023) - [i4]Omar Chehab, Aapo Hyvärinen, Andrej Risteski:
Provable benefits of annealing for estimating normalizing constants: Importance Sampling, Noise-Contrastive Estimation, and beyond. CoRR abs/2310.03902 (2023) - 2022
- [c2]Omar Chehab, Alexandre Gramfort, Aapo Hyvärinen:
The optimal noise in noise-contrastive learning is not what you think. UAI 2022: 307-316 - [i3]Omar Chehab, Alexandre Gramfort, Aapo Hyvärinen:
The Optimal Noise in Noise-Contrastive Learning Is Not What You Think. CoRR abs/2203.01110 (2022) - 2021
- [c1]Alexandre Gramfort, Hubert J. Banville, Omar Chehab, Aapo Hyvärinen, Denis A. Engemann:
Learning with self-supervision on EEG data. BCI 2021: 1-2 - [i2]Omar Chehab, Alexandre Défossez, Jean-Christophe Loiseau, Alexandre Gramfort, Jean-Remi King:
Deep Recurrent Encoder: A scalable end-to-end network to model brain signals. CoRR abs/2103.02339 (2021) - 2020
- [i1]Hubert J. Banville, Omar Chehab, Aapo Hyvärinen, Denis-Alexander Engemann, Alexandre Gramfort:
Uncovering the structure of clinical EEG signals with self-supervised learning. CoRR abs/2007.16104 (2020)
Coauthor Index
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