Institut de Physique Théorique
Direction de la Recherche Fondamentale  -  Saclay
UMR 3681 - INP
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Vendredi 17 novembre 2017

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Séminaire de physique statistique
Lundi 02/10/2017, 11h00
Salle Claude Itzykson, Bât. 774, Orme des Merisiers
Spectral learning of Restricted Boltzmann Machines
Aurélien Decelle
Lab. de Recherche en Informatique, Univ. Paris Sud, Orsay

In this presentation I will expose our recent results on the Restricted Boltzman Machine (RBM). The RBM is a generative model very similar to the Ising model, it is composed of both visible and hidden binary variables, and traditionally used in the context of machine learning. In this context, the goal is to inferred the parameters of the RBM such that it reproduces correctly a dataset's distribution. Although they have been widely used in computer science, the phase diagram of this model is not known precisely in the context of learning. In particular, it is not known how the parameters influence the learning, and what exactly is learned within the parameters of the model. In our work, we show how the SVD of the data governs the first phase of the learning and how this decomposition helps to dynamics and the equilibrium properties of the model.


993459.pdf (16 Ko)

Contact : Loic BERVAS

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