208 citations · 1.7k across the 69 of their papers we have counts for
6 papers · 1 filter
Learning Maximally Monotone Operators for Image Recovery
Jean-Christophe Pesquet, Audrey Repetti, Matthieu Terris +1
We introduce a new paradigm for solving regularized variational problems. These are typically formulated to address ill-posed inverse problems encountered in signal and image proce…
Sparse Signal Reconstruction for Nonlinear Models via Piecewise Rational Optimization
Arthur Marmin, Marc Castella, Jean-Christophe Pesquet +1
We propose a method to reconstruct sparse signals degraded by a nonlinear distortion and acquired at a limited sampling rate. Our method formulates the reconstruction problem as a…
Modeling Electrical Motor Dynamics using Encoder-Decoder with Recurrent Skip Connection
Sagar Verma, Nicolas Henwood, Marc Castella +2
Electrical motors are the most important source of mechanical energy in the industrial world. Their modeling traditionally relies on a physics-based approach, which aims at taking…
Fixed Point Strategies in Data Science
Patrick L. Combettes, Jean-Christophe Pesquet
The goal of this paper is to promote the use of fixed point strategies in data science by showing that they provide a simplifying and unifying framework to model, analyze, and solv…
Deep Transform and Metric Learning Network: Wedding Deep Dictionary Learning and Neural Networks
Wen Tang, Emilie Chouzenoux, Jean-Christophe Pesquet +1
On account of its many successes in inference tasks and denoising applications, Dictionary Learning (DL) and its related sparse optimization problems have garnered a lot of researc…
SPOQ -Over- Regularization for Sparse Signal Recovery applied to Mass Spectrometry
Afef Cherni, Emilie Chouzenoux, Laurent Duval +1
Underdetermined or ill-posed inverse problems require additional information for \ldd{d} sound solutions with tractable optimization algorithms. Sparsity yields consequent heuristi…