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  • Videos (38)
    Majorization-Minimization Subspace Algorithms for Large Scale Data Processing
    01:07:15
    published on June 23, 2017

    Majorization-Minimization Subspace Algorithms for Large Scale Data Processing

    By Emilie Chouzenoux

    IHP

    ... Majorization-Minimization (MM) approaches have become increasingly popular recently, in both signal/image processing and machine learning areas. Our talk will present new theoretical and practical results regarding the MM subspace algorithm [1], where the update of each iterate is restricted to a ...

    Appears in collection : Structured Regularization Summer School - 19-22/06/2017

    Missing fields :

    Score: 17.230658
    Covariant LEAst-square Re-fitting for Image Restoration
    41:11
    published on March 11, 2019

    Covariant LEAst-square Re-fitting for Image Restoration

    By Nicolas Papadakis

    IHP

    ... talk, a framework to remove parts of the systematic errors affecting popular restoration algorithms is presented, with a special focus on image processing tasks. Generalizing ideas that emerged for $\ell_1$ regularization, an approach re-fitting the results of standard methods towards the input ...

    Appears in collection : Variational methods and optimization in imaging

    Missing fields :

    Score: 16.866188
    An atomic norm perspective on total variation regularization in image processing
    42:20
    published on March 12, 2019

    An atomic norm perspective on total variation regularization in image processing

    By Vincent Duval

    IHP

    It is folklore knowledge that the total (gradient) variation regularization tends to promote piecewise constant ``cartoon-like'' images. In this talk I will relate that property to the description of the extreme points of the total variation unit ball. These extreme points have been ...

    Appears in collection : Variational methods and optimization in imaging

    Missing fields :

    Score: 15.55073
    Normalité asymptotique des vecteurs propres de graphes d-réguliers aléatoires, d'après Ágnes Backhausz et Balázs Szegedy
    00:00
    published on October 1, 2018

    Normalité asymptotique des vecteurs propres de graphes d-réguliers aléatoires, d'après Ágnes Backhausz et Balázs Szegedy

    By Charles Bordenave

    IHP

    Soit P l’ensemble des matrices symétriques de taille n avec des entrées dans {0,1}, nulles sur la diagonale et dont la somme de chaque ligne est égale à d (avec dn pair). Un élément de P est la matrice d’adjacence d’un graphe simple à n sommets et d-régulier. Soient A une matrice ...

    Appears in collection : Bourbaki - 20 octobre 2018

    Missing fields : image processing

    Score: 14.830742
    Optimization - lecture 1
    published on February 22, 2021

    Optimization - lecture 1

    By Nelly Pustelnik

    CIRM

    ... Since 2003, convex optimization has become the main thrust behind significant advances in signal processing, image processing and machine learning. The increasingly complex variational formulations encountered in these areas which may involve a sum of several, possibly non-smooth, convex terms, ...

    Appears in collection : Mathematics, Signal Processing and Learning / Mathématiques, traitement du signal et apprentissage

    Missing fields :

    Score: 14.594349
    Optimization - lecture 3
    published on February 22, 2021

    Optimization - lecture 3

    By Nelly Pustelnik

    CIRM

    ... Since 2003, convex optimization has become the main thrust behind significant advances in signal processing, image processing and machine learning. The increasingly complex variational formulations encountered in these areas which may involve a sum of several, possibly non-smooth, convex terms, ...

    Appears in collection : Mathematics, Signal Processing and Learning / Mathématiques, traitement du signal et apprentissage

    Missing fields :

    Score: 14.594349
    Optimization - lecture 4
    published on February 22, 2021

    Optimization - lecture 4

    By Nelly Pustelnik

    CIRM

    ... Since 2003, convex optimization has become the main thrust behind significant advances in signal processing, image processing and machine learning. The increasingly complex variational formulations encountered in these areas which may involve a sum of several, possibly non-smooth, convex terms, ...

    Appears in collection : Mathematics, Signal Processing and Learning / Mathématiques, traitement du signal et apprentissage

    Missing fields :

    Score: 14.594349
    From the modelization of direct problems in image processing to the resolution of inverse problems
    41:51
    published on March 12, 2019

    From the modelization of direct problems in image processing to the resolution of inverse problems

    By Caroline Chaux

    IHP

    In this work, we are interested in the resolution of inverse problems raised in many image processing applications. We considered inverse problems starting from models (understanding the acquisition process), then addressing their resolution (formulated as an optimization problem) while considering ...

    Appears in collection : Variational methods and optimization in imaging

    Missing fields :

    Score: 14.365935
  • Collections (1)
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