Statut  Confirmé 
Série  IPHTSTA 
Domaines  condmat 
Date  Lundi 2 Octobre 2017 
Heure  11:00 
Institut  IPHT 
Salle  Salle Claude Itzykson, Bât. 774 
Nom de l'orateur  Aurélien Decelle 
Prenom de l'orateur  
Addresse email de l'orateur  
Institution de l'orateur  Lab. de Recherche en Informatique, Univ. Paris Sud, Orsay 
Titre  Spectral learning of Restricted Boltzmann Machines 
Résumé  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. 
Numéro de preprint arXiv  
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