8 papers
Variable Bregman Majorization-Minimization algorithms for nonconvex nonsmooth optimization, with application to Poisson imaging
Maxence Adly, Alix Chazottes, Emilie Chouzenoux +2
In this work, we introduce a unifying Bregman-based majorization-minimization (MM) framework for solving nonconvex nonsmooth optimization problems. The proposed approach leverages…
Stability Bounds for the Unfolded Forward-Backward Algorithm
Emilie Chouzenoux, Cecile Della Valle, Jean-Christophe Pesquet
We consider a neural network architecture designed to solve inverse problems where the degradation operator is linear and known. This architecture is constructed by unrolling a for…
UNEM: UNrolled Generalized EM for Transductive Few-Shot Learning
Long Zhou, Fereshteh Shakeri, Aymen Sadraoui +3
Transductive few-shot learning has recently triggered wide attention in computer vision. Yet, current methods introduce key hyper-parameters, which control the prediction statistic…
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…
An adaptive forward-backward-forward splitting algorithm for solving pseudo-monotone inclusions
Flavia Chorobura, Ion Necoara, Jean-Christophe Pesquet
In this paper, we propose an adaptive forward-backward-forward splitting algorithm for finding a zero of a pseudo-monotone operator which is split as a sum of three operators: the…
Variable Bregman Majorization-Minimization Algorithm and its Application to Dirichlet Maximum Likelihood Estimation
Ségolène Martin, Jean-Christophe Pesquet, Gabriele Steidl +1
We propose a novel Bregman descent algorithm for minimizing a convex function that is expressed as the sum of a differentiable part (defined over an open set) and a possibly nonsmo…