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  • Videos (36)
    Matrix and graph estimation 3
    01:32:20
    published on June 22, 2017

    Matrix and graph estimation 3

    By Andrea Montanari

    IHP

    ... 1) Random matrix theory and spectral methods. 2) The semidefinite programming approach to graph clustering. 3) Local algorithms and graphical models. The hidden clique problem. 4) Non-negative matrix factorization.

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

    Score: 28.814737
    Matrix and graph estimation 1
    01:34:23
    published on June 21, 2017

    Matrix and graph estimation 1

    By Andrea Montanari

    IHP

    ... 1) Random matrix theory and spectral methods. 2) The semidefinite programming approach to graph clustering. 3) Local algorithms and graphical models. The hidden clique problem. 4) Non-negative matrix factorization.

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

    Score: 28.814737
    The Role of the Transpose in Free Probability: the partial transpose of R-cyclic operators
    43:24
    published on November 6, 2017

    The Role of the Transpose in Free Probability: the partial transpose of R-cyclic operators

    By James Mingo

    IHP

    ... With random matrix models, we usually need tensor independence of the entries and some kind of group invariance of the joint distribution of the entries to get the (asymptotic) freeness necessary to apply the tools of free probability. A few years ago Mihai Popa and I found that the transpose also ...

    Appears in collection : Probabilistic techniques and Quantum Information Theory

    Score: 20.711004
    Inverted steady states and LAMP models
    50:14
    published on October 29, 2018

    Inverted steady states and LAMP models

    By Andrew Tomkins

    IHES

    ... Second, we turn to situations in which the markov assumptionis too restrictive, as effective models must retain some information about the more distant past. We describe LAMP: linear additive markov processes, which extend markov chains to take into account the entire history of the process, while ...

    Appears in collection : Google matrix: fundamentals, applications and beyond

    Score: 19.139727
    Matrix and graph estimation 4
    01:29:45
    published on June 26, 2017

    Matrix and graph estimation 4

    By Andrea Montanari

    IHP

    ... 1) Random matrix theory and spectral methods. 2) The semidefinite programming approach to graph clustering. 3) Local algorithms and graphical models. The hidden clique problem. 4) Non-negative matrix factorization.

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

    Score: 17.795647
    Matrix and graph estimation 2
    01:27:14
    published on June 22, 2017

    Matrix and graph estimation 2

    By Andrea Montanari

    IHP

    ... 1) Random matrix theory and spectral methods. 2) The semidefinite programming approach to graph clustering. 3) Local algorithms and graphical models. The hidden clique problem. 4) Non-negative matrix factorization.

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

    Score: 17.795647
    Recommender Systems: Twenty years of research
    56:34
    published on October 30, 2018

    Recommender Systems: Twenty years of research

    By Lior Rokach

    IHES

    https://indico.math.cnrs.fr/event/3475/attachments/2180/2560/Rokach-GomaxSlides.pptx

    Appears in collection : Google matrix: fundamentals, applications and beyond

    Missing fields : models

    Score: 16.549276
    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 : matrix models

    Score: 15.595985
  • Collections (1)
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