248 citations · 771 across the 28 of their papers we have counts for
9 papers · 1 filter
Learning truly monotone operators with applications to nonlinear inverse problems
Younes Belkouchi, Jean-Christophe Pesquet, Audrey Repetti +1
This article introduces a novel approach to learning monotone neural networks through a newly defined penalization loss. The proposed method is particularly effective in solving cl…
A multilevel framework for accelerating uSARA in radio-interferometric imaging
Guillaume Lauga, Audrey Repetti, Elisa Riccietti +3
This paper presents a multilevel algorithm specifically designed for radio-interferometric imaging in astronomy. The proposed algorithm is used to solve the uSARA (unconstrained Sp…
A new non-convex framework to improve asymptotical knowledge on generic stochastic gradient descent
Jean-Baptiste Fest, Audrey Repetti, Emilie Chouzenoux
Stochastic gradient optimization methods are broadly used to minimize non-convex smooth objective functions, for instance when training deep neural networks. However, theoretical g…
A stochastic use of the Kurdyka-Lojasiewicz property: Investigation of optimization algorithms behaviours in a non-convex differentiable framework
Jean-Baptiste Fest, Audrey Repetti, Emilie Chouzenoux
Stochastic differentiable approximation schemes are widely used for solving high dimensional problems. Most of existing methods satisfy some desirable properties, including conditi…
A Variational Inequality Model for Learning Neural Networks
Patrick L. Combettes, Jean-Christophe Pesquet, Audrey Repetti
Neural networks have become ubiquitous tools for solving signal and image processing problems, and they often outperform standard approaches. Nevertheless, training neural networks…
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…
Variable Metric Forward-Backward Algorithm for Composite Minimization Problems
Audrey Repetti, Yves Wiaux
We present a forward-backward-based algorithm to minimize a sum of a differentiable function and a nonsmooth function, both being possibly nonconvex. The main contribution of this…
Euclid in a Taxicab: Sparse Blind Deconvolution with Smoothed l1/l2 Regularization
Audrey Repetti, Mai Quyen Pham, Laurent Duval +2
The l1/l2 ratio regularization function has shown good performance for retrieving sparse signals in a number of recent works, in the context of blind deconvolution. Indeed, it bene…
A Class of Randomized Primal-Dual Algorithms for Distributed Optimization
Jean-Christophe Pesquet, Audrey Repetti
Based on a preconditioned version of the randomized block-coordinate forward-backward algorithm recently proposed in [Combettes,Pesquet,2014], several variants of block-coordinate…