208 citations · 1.7k across the 69 of their papers we have counts for
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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…
Towards Practical Few-Shot Query Sets: Transductive Minimum Description Length Inference
Ségolène Martin, Malik Boudiaf, Emilie Chouzenoux +2
Standard few-shot benchmarks are often built upon simplifying assumptions on the query sets, which may not always hold in practice. In particular, for each task at testing time, th…
Efficient Bayes Inference in Neural Networks through Adaptive Importance Sampling
Yunshi Huang, Emilie Chouzenoux, Victor Elvira +1
Bayesian neural networks (BNNs) have received an increased interest in the last years. In BNNs, a complete posterior distribution of the unknown weight and bias parameters of the n…
A CNC approach for Directional Total Variation
Gabriele Scrivanti, Emilie Chouzenoux, Jean-Christophe Pesquet
The core of many approaches for the resolution of variational inverse problems arising in signal and image processing consists of promoting the sought solution to have a sparse rep…
Deep Unfolding of the DBFB Algorithm with Application to ROI CT Imaging with Limited Angular Density
Marion Savanier, Emilie Chouzenoux, Jean-Christophe Pesquet +1
This paper presents a new method for reconstructing regions of interest (ROI) from a limited number of computed tomography (CT) measurements. Classical model-based iterative recons…
A Variational Approach for Joint Image Recovery and Feature Extraction Based on Spatially-Varying Generalised Gaussian Models
Emilie Chouzenoux, Marie-Caroline Corbineau, Jean-Christophe Pesquet +1
The joint problem of reconstruction / feature extraction is a challenging task in image processing. It consists in performing, in a joint manner, the restoration of an image and th…