Search Results for author: Jean-Christophe Pesquet

Found 40 papers, 11 papers with code

Primary liver cancer classification from routine tumour biopsy using weakly supervised deep learning

no code implementations7 Apr 2024 Aurélie Beaufrère, Nora Ouzir, Paul Emile Zafar, Astrid Laurent-Bellue, Miguel Albuquerque, Gwladys Lubuela, Jules Grégory, Catherine Guettier, Kévin Mondet, Jean-Christophe Pesquet, Valérie Paradis

The diagnostic agreement between the pathological diagnosis and the model predictions in the internal and external validation sets was 100% (11/11) and 96% (25/26) for HCC and 78% (7/9) and 87% (13/15) for iCCA, respectively.

Clustering Weakly-supervised Learning

Learning truly monotone operators with applications to nonlinear inverse problems

no code implementations30 Mar 2024 Younes Belkouchi, Jean-Christophe Pesquet, Audrey Repetti, Hugues Talbot

This article introduces a novel approach to learning monotone neural networks through a newly defined penalization loss.

Transductive Zero-Shot and Few-Shot CLIP

1 code implementation CVPR 2024 Ségolène Martin, Yunshi Huang, Fereshteh Shakeri, Jean-Christophe Pesquet, Ismail Ben Ayed

Transductive inference has been widely investigated in few-shot image classification but completely overlooked in the recent fast growing literature on adapting vision-langage models like CLIP.

Classification Few-Shot Image Classification

Convex Parameter Estimation of Perturbed Multivariate Generalized Gaussian Distributions

no code implementations12 Dec 2023 Nora Ouzir, Frédéric Pascal, Jean-Christophe Pesquet

In robust estimation, imposing classical constraints on the precision matrix, such as sparsity, has been limited by the non-convexity of the resulting cost function.

Aggregated f-average Neural Network for Interpretable Ensembling

no code implementations9 Oct 2023 Mathieu Vu, Emilie Chouzenoux, Jean-Christophe Pesquet, Ismail Ben Ayed

Ensemble learning leverages multiple models (i. e., weak learners) on a common machine learning task to enhance prediction performance.

Ensemble Learning Few-Shot Class-Incremental Learning +1

Majorization-Minimization for sparse SVMs

no code implementations31 Aug 2023 Alessandro Benfenati, Emilie Chouzenoux, Giorgia Franchini, Salla Latva-Aijo, Dominik Narnhofer, Jean-Christophe Pesquet, Sebastian J. Scott, Mahsa Yousefi

Several decades ago, Support Vector Machines (SVMs) were introduced for performing binary classification tasks, under a supervised framework.

Binary Classification

A primal-dual data-driven method for computational optical imaging with a photonic lantern

1 code implementation20 Jun 2023 Carlos Santos Garcia, Mathilde Larchevêque, Solal O'Sullivan, Martin Van Waerebeke, Robert R. Thomson, Audrey Repetti, Jean-Christophe Pesquet

A proximal algorithm based on a sparsity prior, dubbed SARA-COIL, has been further proposed to solve the associated inverse problem, to enable image reconstructions for high resolution COIL microendoscopy.

Decision Making Learning Theory

Proximal Splitting Adversarial Attack for Semantic Segmentation

1 code implementation CVPR 2023 Jérôme Rony, Jean-Christophe Pesquet, Ismail Ben Ayed

Classification has been the focal point of research on adversarial attacks, but only a few works investigate methods suited to denser prediction tasks, such as semantic segmentation.

Adversarial Attack Segmentation +1

Towards Practical Few-Shot Query Sets: Transductive Minimum Description Length Inference

1 code implementation26 Oct 2022 Ségolène Martin, Malik Boudiaf, Emilie Chouzenoux, Jean-Christophe Pesquet, Ismail Ben Ayed

We relax these assumptions and extend current benchmarks, so that the query-set classes of a given task are unknown, but just belong to a much larger set of possible classes.

Deep Unfolding of the DBFB Algorithm with Application to ROI CT Imaging with Limited Angular Density

no code implementations27 Sep 2022 Marion Savanier, Emilie Chouzenoux, Jean-Christophe Pesquet, Cyril Riddell

We unfold the Dual Block coordinate Forward-Backward (DBFB) algorithm, embedded in an iterative reweighted scheme, allowing the learning of key parameters in a supervised manner.

Computed Tomography (CT) Image Reconstruction

Proximal Splitting Adversarial Attacks for Semantic Segmentation

1 code implementation14 Jun 2022 Jérôme Rony, Jean-Christophe Pesquet, Ismail Ben Ayed

Classification has been the focal point of research on adversarial attacks, but only a few works investigate methods suited to denser prediction tasks, such as semantic segmentation.

Adversarial Attack Segmentation +1

Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution

1 code implementation14 Oct 2021 Yunshi Huang, Emilie Chouzenoux, Jean-Christophe Pesquet

In this paper, we introduce a variational Bayesian algorithm (VBA) for image blind deconvolution.

Rolling Shutter Correction

Deep Transform and Metric Learning Networks

no code implementations21 Apr 2021 Wen Tang, Emilie Chouzenoux, Jean-Christophe Pesquet, Hamid Krim

Based on its great successes in inference and denosing tasks, Dictionary Learning (DL) and its related sparse optimization formulations have garnered a lot of research interest.

Dictionary Learning Metric Learning

Sparsifying Networks via Subdifferential Inclusion

no code implementations1 Jan 2021 Sagar Verma, Jean-Christophe Pesquet

Sparsifying deep neural networks is of paramount interest in many areas, especially when those networks have to be implemented on low-memory devices.

Image Classification speech-recognition +4

Learning Maximally Monotone Operators for Image Recovery

2 code implementations24 Dec 2020 Jean-Christophe Pesquet, Audrey Repetti, Matthieu Terris, Yves Wiaux

Recently, several works have proposed to replace the operator related to the regularization by a more sophisticated denoiser.

Automated Theorem Proving Image Restoration Optimization and Control Image and Video Processing 47H05, 90C25, 90C59, 65K10, 49M27, 68T07, 68U10, 94A08

Sparse Signal Reconstruction for Nonlinear Models via Piecewise Rational Optimization

no code implementations29 Oct 2020 Arthur Marmin, Marc Castella, Jean-Christophe Pesquet, Laurent Duval

We propose a method to reconstruct sparse signals degraded by a nonlinear distortion and acquired at a limited sampling rate.

Modeling Electrical Motor Dynamics using Encoder-Decoder with Recurrent Skip Connection

no code implementations8 Oct 2020 Sagar Verma, Nicolas Henwood, Marc Castella, Francois Malrait, Jean-Christophe Pesquet

In this paper, we explore the feasibility of modeling the dynamics of an electrical motor by following a data-driven approach, which uses only its inputs and outputs and does not make any assumption on its internal behaviour.

Decoder Domain Adaptation

Fixed Point Strategies in Data Science

no code implementations5 Aug 2020 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 solve a great variety of problems.

Optimization and Control

Deep Transform and Metric Learning Network: Wedding Deep Dictionary Learning and Neural Networks

no code implementations18 Feb 2020 Wen Tang, Emilie Chouzenoux, Jean-Christophe Pesquet, Hamid Krim

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 research interest.

Denoising Dictionary Learning +1

General risk measures for robust machine learning

no code implementations26 Apr 2019 Emilie Chouzenoux, Henri Gérard, Jean-Christophe Pesquet

A wide array of machine learning problems are formulated as the minimization of the expectation of a convex loss function on some parameter space.

BIG-bench Machine Learning

Lipschitz Certificates for Neural Network Structures Driven by Averaged Activation Operators

no code implementations3 Mar 2019 Patrick L. Combettes, Jean-Christophe Pesquet

Deriving sharp Lipschitz constants for feed-forward neural networks is essential to assess their robustness in the face of adversarial inputs.

Optimization and Control

Deep Unfolding of a Proximal Interior Point Method for Image Restoration

1 code implementation11 Dec 2018 Carla Bertocchi, Emilie Chouzenoux, Marie-Caroline Corbineau, Jean-Christophe Pesquet, Marco Prato

Variational methods are widely applied to ill-posed inverse problems for they have the ability to embed prior knowledge about the solution.

Deblurring Image Deblurring +1

Deep Neural Network Structures Solving Variational Inequalities

no code implementations22 Aug 2018 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.

Optimization and Control

A Random Block-Coordinate Douglas-Rachford Splitting Method with Low Computational Complexity for Binary Logistic Regression

no code implementations25 Dec 2017 Luis M. Briceno-Arias, Giovanni Chierchia, Emilie Chouzenoux, Jean-Christophe Pesquet

In this paper, we propose a new optimization algorithm for sparse logistic regression based on a stochastic version of the Douglas-Rachford splitting method.


A Fast Algorithm Based on a Sylvester-like Equation for LS Regression with GMRF Prior

no code implementations18 Sep 2017 Qi Wei, Emilie Chouzenoux, Jean-Yves Tourneret, Jean-Christophe Pesquet

This paper presents a fast approach for penalized least squares (LS) regression problems using a 2D Gaussian Markov random field (GMRF) prior.


Image Analysis Using a Dual-Tree $M$-Band Wavelet Transform

no code implementations27 Feb 2017 Caroline Chaux, Laurent Duval, Jean-Christophe Pesquet

We propose a 2D generalization to the $M$-band case of the dual-tree decomposition structure (initially proposed by N. Kingsbury and further investigated by I. Selesnick) based on a Hilbert pair of wavelets.

Denoising Tree Decomposition

A Variational Bayesian Approach for Image Restoration. Application to Image Deblurring with Poisson-Gaussian Noise

no code implementations24 Oct 2016 Yosra Marnissi, Yuling Zheng, Emilie Chouzenoux, Jean-Christophe Pesquet

We demonstrate the potential of the proposed approach through comparisons with state-of-the-art techniques that are specifically tailored to signal recovery in the presence of mixed Poisson-Gaussian noise.

Deblurring Image Deblurring +1

A Proximal Approach for Sparse Multiclass SVM

no code implementations15 Jan 2015 G. Chierchia, Nelly Pustelnik, Jean-Christophe Pesquet, B. Pesquet-Popescu

In this paper, we propose a convex optimization approach for efficiently and exactly solving the multiclass SVM learning problem involving a sparse regularization and the multiclass hinge loss formulated by Crammer and Singer.

A Class of Randomized Primal-Dual Algorithms for Distributed Optimization

1 code implementation24 Jun 2014 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 primal-dual algorithms are designed in order to solve a wide array of monotone inclusion problems.

Optimization and Control 47H05, 49M29, 49M27, 65K10, 90C25

Playing with Duality: An Overview of Recent Primal-Dual Approaches for Solving Large-Scale Optimization Problems

no code implementations20 Jun 2014 Nikos Komodakis, Jean-Christophe Pesquet

Optimization methods are at the core of many problems in signal/image processing, computer vision, and machine learning.

A Non-Local Structure Tensor Based Approach for Multicomponent Image Recovery Problems

no code implementations21 Mar 2014 Giovanni Chierchia, Nelly Pustelnik, Beatrice Pesquet-Popescu, Jean-Christophe Pesquet

In this paper, we extend the NLTV-based regularization to multicomponent images by taking advantage of the Structure Tensor (ST) resulting from the gradient of a multicomponent image.

Spatio-temporal wavelet regularization for parallel MRI reconstruction: application to functional MRI

no code implementations23 Dec 2011 Lotfi Chaari, Sébastien Mériaux, Jean-Christophe Pesquet, Philippe Ciuciu

To improve the performance of the widely used SENSE algorithm, 2D- or slice-specific regularization in the wavelet domain has been deeply investigated.

MRI Reconstruction

Primal-dual splitting algorithm for solving inclusions with mixtures of composite, Lipschitzian, and parallel-sum monotone operators

no code implementations30 Jun 2011 Patrick L. Combettes, Jean-Christophe Pesquet

We propose a primal-dual splitting algorithm for solving monotone inclusions involving a mixture of sums, linear compositions, and parallel sums of set-valued and Lipschitzian operators.

Optimization and Control 47H05, 90C25

Proximal Splitting Methods in Signal Processing

1 code implementation17 Dec 2009 Patrick L. Combettes, Jean-Christophe Pesquet

The proximity operator of a convex function is a natural extension of the notion of a projection operator onto a convex set.

Optimization and Control Numerical Analysis 90C25, 65K05, 90C90, 94A08

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