Canal-U

Mon compte

Résultats de recherche

Nombre de programmes trouvés : 819
Cours magistraux

le (1h1m44s)

Tropp 5/9 - Random matrix theory and computational linear algebra

This course treats some contemporary algorithms from computational linear algebra that involve random matrices. Rather than surveying the entire field, we focus on a few algorithms that are both simple and practically useful. We begin with an introduction to matrix concentration inequalities, which are a powerful tool for analyzing structured random matrices. We use these ideas to study matrix approximations constructed via randomized sampling, such as the random features method. As a more sophisticated application, we present a complete treatment of a recent algorithm for solving graph Laplacian linear systems in near-linear time. Some references : 1. Tropp, "An introduction to ...
Voir la vidéo
Cours magistraux

le (56m12s)

Tropp 6/9 - Random matrix theory and computational linear algebra

This course treats some contemporary algorithms from computational linear algebra that involve random matrices. Rather than surveying the entire field, we focus on a few algorithms that are both simple and practically useful. We begin with an introduction to matrix concentration inequalities, which are a powerful tool for analyzing structured random matrices. We use these ideas to study matrix approximations constructed via randomized sampling, such as the random features method. As a more sophisticated application, we present a complete treatment of a recent algorithm for solving graph Laplacian linear systems in near-linear time. Some references : 1. Tropp, "An introduction to ...
Voir la vidéo
Cours magistraux

le (0s)

Tropp 7/9 - Random matrix theory and computational linear algebra

This course treats some contemporary algorithms from computational linear algebra that involve random matrices. Rather than surveying the entire field, we focus on a few algorithms that are both simple and practically useful. We begin with an introduction to matrix concentration inequalities, which are a powerful tool for analyzing structured random matrices. We use these ideas to study matrix approximations constructed via randomized sampling, such as the random features method. As a more sophisticated application, we present a complete treatment of a recent algorithm for solving graph Laplacian linear systems in near-linear time. Some references : 1. Tropp, "An introduction to ...
Voir la vidéo
Cours magistraux

le (1h24m41s)

Tropp 8/9 - Random matrix theory and computational linear algebra

This course treats some contemporary algorithms from computational linear algebra that involve random matrices. Rather than surveying the entire field, we focus on a few algorithms that are both simple and practically useful. We begin with an introduction to matrix concentration inequalities, which are a powerful tool for analyzing structured random matrices. We use these ideas to study matrix approximations constructed via randomized sampling, such as the random features method. As a more sophisticated application, we present a complete treatment of a recent algorithm for solving graph Laplacian linear systems in near-linear time. Some references : 1. Tropp, "An introduction to ...
Voir la vidéo
Cours magistraux

le (41m0s)

Tropp 9/9 - Random matrix theory and computational linear algebra

This course treats some contemporary algorithms from computational linear algebra that involve random matrices. Rather than surveying the entire field, we focus on a few algorithms that are both simple and practically useful. We begin with an introduction to matrix concentration inequalities, which are a powerful tool for analyzing structured random matrices. We use these ideas to study matrix approximations constructed via randomized sampling, such as the random features method. As a more sophisticated application, we present a complete treatment of a recent algorithm for solving graph Laplacian linear systems in near-linear time. Some references : 1. Tropp, "An introduction to ...
Voir la vidéo
Label UNT Vidéocours

le (26m7s)

Analyse, intégrales multiples

Série de "zooms" transversaux qui permettent aux étudiants de faire une synthèse de leurs connaissances et de vérifier qu'ils ont acquis les concepts fondamentaux. Aide à la compréhension des concepts de bases et des techniques fondamentales.GénériqueMATHÉMATIQUES DEUG 2ème ANNÉE Émission conçue et préparée par Jacques VAUTHIER Centre de Télé-enseignement Universitaire Université Pierre et Marie Curie - Paris 6 Émission réalisée avec le soutien du Ministère de l'Education Nationale de la Recherche et des Technologies Direction de la Technologie et le concours de la Fédération Interuniversitaire d'Enseignement à Distance Production déléguée et exécutive Université Nancy 2 - VIDEOSCOP Université Paris 6 ...
Voir la vidéo
Conférences

le (39m34s)

Klopp - Sparse Network Estimation

Inhomogeneous random graph models encompass many network models such as stochastic block models and latent position models. We consider the problem of the statistical estimation of the matrix of connection probabilities based on the observations of the adjacency matrix of the network. We will also discuss the problem of graphon estimation when the probability matrix is sampled according to the graphon model. For these two problems, the minimax optimal rates of convergence in Frobenius norm are achieved by the least squares estimator which is known to be NP-hard. In this talk we will present two alternatives to the least squares: ...
Voir la vidéo

 
FMSH
 
Facebook Twitter
Mon Compte