13 citations · 32 across the 7 of their papers we have counts for
8 papers
Sliced-Wasserstein on Symmetric Positive Definite Matrices for M/EEG Signals
Clément Bonet, Benoît Malézieux, Alain Rakotomamonjy +4
When dealing with electro or magnetoencephalography records, many supervised prediction tasks are solved by working with covariance matrices to summarize the signals. Learning with…
A Probabilistic Autoencoder for Type Ia Supernovae Spectral Time Series
George Stein, Uros Seljak, Vanessa Bohm +37
We construct a physically-parameterized probabilistic autoencoder (PAE) to learn the intrinsic diversity of type Ia supernovae (SNe Ia) from a sparse set of spectral time series. T…
Understanding approximate and unrolled dictionary learning for pattern recovery
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. Alternating minimization (AM)…
MIP and Set Covering approaches for Sparse Approximation
Diego Delle Donne, Matthieu Kowalski, Leo Liberti
The Sparse Approximation problem asks to find a solution such that , for a given norm , minimizing the size of the support $||x||_0 := \#\{j \ |\ x_j…
Estimation with Low-Rank Time-Frequency Synthesis Models
Cédric Févotte, Matthieu Kowalski
Many state-of-the-art signal decomposition techniques rely on a low-rank factorization of a time-frequency (t-f) transform. In particular, nonnegative matrix factorization (NMF) of…
Water Residence Time Estimation by 1D Deconvolution in the Form of a l2-Regularized Inverse Problem With Smoothness, Positivity and Causality Constraints
Alina G. Meresescu, Matthieu Kowalski, Frédéric Schmidt +1
The Water Residence Time distribution is the equivalent of the impulse response of a linear system allowing the propagation of water through a medium, e.g. the propagation of rain…
Social-sparsity brain decoders: faster spatial sparsity
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. H…
Convex Optimization approach to signals with fast varying instantaneous frequency
Matthieu Kowalski, Adrien Meynard, Hau-tieng Wu
Motivated by the limitation of analyzing oscillatory signals composed of multiple components with fast-varying instantaneous frequency, we approach the time-frequency analysis prob…