no code implementations • 24 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.
no code implementations • 18 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.
no code implementations • 25 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.
no code implementations • 23 May 2018 • Viacheslav Dudar, Giovanni Chierchia, Emilie Chouzenoux, Jean-Christophe Pesquet, Vladimir Semenov
In this paper, we develop a novel second-order method for training feed-forward neural nets.
1 code implementation • 11 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.
no code implementations • 26 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.
no code implementations • 10 Dec 2019 • Aanchal Mongia, Neha Jhamb, Emilie Chouzenoux, Angshul Majumdar
Latent factor models have been used widely in collaborative filtering based recommender systems.
no code implementations • 10 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.
no code implementations • 18 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.
1 code implementation • 3 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
1 code implementation • 22 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.
no code implementations • 2 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).
no code implementations • 9 Nov 2020 • Pooja Gupta, Angshul Majumdar, Emilie Chouzenoux, Giovanni Chierchia
This work proposes a supervised multi-channel time-series learning framework for financial stock trading.
no code implementations • 9 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.
1 code implementation • 9 Nov 2020 • Pooja Gupta, Jyoti Maggu, Angshul Majumdar, Emilie Chouzenoux, Giovanni Chierchia
This work addresses the problem of analyzing multi-channel time series data %.
no code implementations • 21 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.
1 code implementation • 14 Oct 2021 • Yunshi Huang, Emilie Chouzenoux, Jean-Christophe Pesquet
In this paper, we introduce a variational Bayesian algorithm (VBA) for image blind deconvolution.
no code implementations • 3 Sep 2022 • Emilie Chouzenoux, Marie-Caroline Corbineau, Jean-Christophe Pesquet, Gabriele Scrivanti
The joint problem of reconstruction / feature extraction is a challenging task in image processing.
no code implementations • 27 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.
1 code implementation • 3 Oct 2022 • Yunshi Huang, Emilie Chouzenoux, Victor Elvira, Jean-Christophe Pesquet
Bayesian neural networks (BNNs) have received an increased interest in the last years.
no code implementations • 19 Oct 2022 • Stuti Jain, Emilie Chouzenoux, Kriti Kumar, Angshul Majumdar
The performance of the proposed method is evaluated through the DrugBank dataset, and comparisons are provided against state-of-the-art techniques.
1 code implementation • 26 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.
1 code implementation • 4 Jan 2023 • Paul Zheng, Emilie Chouzenoux, Laurent Duval
Denoising, detrending, deconvolution: usual restoration tasks, traditionally decoupled.
1 code implementation • 4 Jul 2023 • Paul Zheng, Emilie Chouzenoux, Laurent Duval
Denoising, detrending, deconvolution: usual restoration tasks, traditionally decoupled.
no code implementations • 6 Jul 2023 • Emilie Chouzenoux, Victor Elvira
This work proposes a novel approach to fill this gap by introducing a joint graphical modeling framework that bridges the static graphical Lasso model and a causal-based graphical approach for the linear-Gaussian SSM.
no code implementations • 31 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.
no code implementations • 9 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.
1 code implementation • 25 Oct 2023 • Thomas Guilmeau, Nicola Branchini, Emilie Chouzenoux, Víctor Elvira
We then show that the $\alpha$-divergence can be approximated by a generalized notion of effective sample size and leverage this new perspective to adapt the tail parameter with Bayesian optimization.
no code implementations • 21 Nov 2023 • Shalini Sharma, Angshul Majumdar, Emilie Chouzenoux, Victor Elvira
We call the proposed approach the deep state-space model.
no code implementations • 30 Nov 2023 • Julien Ajdenbaum, Emilie Chouzenoux, Claire Lefort, Ségolène Martin, Jean-Christophe Pesquet
This makes the restoration of MPM images challenging.