1 code implementation • 23 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.
1 code implementation • 2 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.
1 code implementation • 3 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.
2 code implementations • 31 Jul 2020 • Hubert Banville, Omar Chehab, Aapo Hyvärinen, Denis-Alexander Engemann, Alexandre Gramfort
Our results suggest that SSL may pave the way to a wider use of deep learning models on EEG data.