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4 papers
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…