208 citations · 1.7k across the 69 of their papers we have counts for
5 papers · 1 filter
Deep Unfolding of a Proximal Interior Point Method for Image Restoration
Carla Bertocchi, Emilie Chouzenoux, Marie-Caroline Corbineau +2
Variational methods are widely applied to ill-posed inverse problems for they have the ability to embed prior knowledge about the solution. However, the level of performance of the…
Deep Neural Network Structures Solving Variational Inequalities
Patrick L. Combettes, Jean-Christophe Pesquet
Motivated by structures that appear in deep neural networks, we investigate nonlinear composite models alternating proximity and affine operators defined on different spaces. We fi…
Rational Optimization for Nonlinear Reconstruction with Approximate Penalization
Marc Castella, Jean-Christophe Pesquet, Arthur Marmin
Recovering nonlinearly degraded signal in the presence of noise is a challenging problem. In this work, this problem is tackled by minimizing the sum of a non convex least-squares…
A Two-Stage Subspace Trust Region Approach for Deep Neural Network Training
Viacheslav Dudar, Giovanni Chierchia, Emilie Chouzenoux +2
In this paper, we develop a novel second-order method for training feed-forward neural nets. At each iteration, we construct a quadratic approximation to the cost function in a low…
A Proximal Approach for a Class of Matrix Optimization Problems
A. Benfenati, E. Chouzenoux, J. -C. Pesquet
In recent years, there has been a growing interest in mathematical models leading to the minimization, in a symmetric matrix space, of a Bregman divergence coupled with a regulariz…