Títol
A spectral learning framework for graphical models

Ponent
Raphaël Bailly

Lloc
Omega-S208

Dia
05/06/2012

Horari
11:00h

Abstract

I will present an extended version of the spectral algorithm which can be applied to graphs. This algorithm can be used as a learning algorithm for graphical models - directed and undirected. It can be used in a density estimation task on a distribution of labelled graphs. This algorithm is proven to converge, and is not prone to local extrema.

 

Slides
slides

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