Search Results for author: Omar Chehab

Found 4 papers, 4 papers with code

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

1 code implementation23 Jan 2023 Omar Chehab, Alexandre Gramfort, Aapo Hyvarinen

Nevertheless, we soberly conclude that the optimal noise may be hard to sample from, and the gain in efficiency can be modest compared to choosing the noise distribution equal to the data's.

Self-Supervised Learning

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

1 code implementation2 Mar 2022 Omar Chehab, Alexandre Gramfort, Aapo Hyvarinen

Learning a parametric model of a data distribution is a well-known statistical problem that has seen renewed interest as it is brought to scale in deep learning.

Contrastive Learning

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

1 code implementation3 Mar 2021 Omar Chehab, Alexandre Defossez, Jean-Christophe Loiseau, Alexandre Gramfort, Jean-Remi King

Understanding how the brain responds to sensory inputs is challenging: brain recordings are partial, noisy, and high dimensional; they vary across sessions and subjects and they capture highly nonlinear dynamics.

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