2 code implementations • 10 Mar 2023 • Clément Bonet, Benoît Malézieux, Alain Rakotomamonjy, Lucas Drumetz, Thomas Moreau, Matthieu Kowalski, Nicolas Courty
When dealing with electro or magnetoencephalography records, many supervised prediction tasks are solved by working with covariance matrices to summarize the signals.
1 code implementation • ICLR 2022 • Benoît Malézieux, Thomas Moreau, Matthieu Kowalski
Dictionary learning consists of finding a sparse representation from noisy data and is a common way to encode data-driven prior knowledge on signals.
no code implementations • 25 Apr 2018 • Cédric Févotte, Matthieu Kowalski
In this paper we instead propose a synthesis approach, where low-rankness is imposed to the synthesis coefficients of the data signal over a given t-f dictionary (such as a Gabor frame).
no code implementations • 21 Jun 2016 • Gaël Varoquaux, Matthieu Kowalski, Bertrand Thirion
Spatially-sparse predictors are good models for brain decoding: they give accurate predictions and their weight maps are interpretable as they focus on a small number of regions.
no code implementations • NeurIPS 2014 • Cédric Févotte, Matthieu Kowalski
Many single-channel signal decomposition techniques rely on a low-rank factorization of a time-frequency transform.