Search Results for author: Emilie Chouzenoux

Found 17 papers, 5 papers with code

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.

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

DeConFuse : A Deep Convolutional Transform based Unsupervised Fusion Framework

no code implementations9 Nov 2020 Pooja Gupta, Jyoti Maggu, Angshul Majumdar, Emilie Chouzenoux, Giovanni Chierchia

This work proposes an unsupervised fusion framework based on deep convolutional transform learning.

Deep Convolutional Transform Learning -- Extended version

no code implementations2 Oct 2020 Jyoti Maggu, Angshul Majumdar, Emilie Chouzenoux, Giovanni Chierchia

This work introduces a new unsupervised representation learning technique called Deep Convolutional Transform Learning (DCTL).

Representation Learning

DeepVir -- Graphical Deep Matrix Factorization for "In Silico" Antiviral Repositioning: Application to COVID-19

1 code implementation22 Sep 2020 Aanchal Mongia, Stuti Jain, Emilie Chouzenoux, Angshul Majumda

This work formulates antiviral repositioning as a matrix completion problem where the antiviral drugs are along the rows and the viruses along the columns.

Frame Matrix Completion

A computational approach to aid clinicians in selecting anti-viral drugs for COVID-19 trials

1 code implementation3 Jul 2020 Aanchal Mongia, Sanjay Kr. Saha, Emilie Chouzenoux, Angshul Majumdar

The main contribution of this work is a manually curated database publicly shared, comprising of existing associations between viruses and their corresponding antivirals.

Quantitative Methods

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

Transformed Subspace Clustering

no code implementations10 Dec 2019 Jyoti Maggu, Angshul Majumdar, Emilie Chouzenoux

We assume that, even if the raw data is not separable into subspac-es, one can learn a representation (transform coef-ficients) such that the learnt representation is sep-arable into subspaces.

Image Clustering

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.

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

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.

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

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