Statut  Confirmé 
Série  COURS 
Domaines  condmat,qbio 
Date  Vendredi 25 Mai 2018 
Heure  10:00 
Institut  IPHT 
Salle  Salle Claude Itzykson, Bât. 774 
Nom de l'orateur  Rémi Monasson 
Prenom de l'orateur  
Addresse email de l'orateur  
Institution de l'orateur  ENS Paris 
Titre  Unsupervised neural networks: from theory to systems biology (3/5) 
Résumé  Artificial neural networks, introduced decades ago, are now key tools for automatic learning from data. This series of six lectures will focus on a few neural network architectures used in the context of unsupervised learning, that is, of unlabeled data. \par In particular we will focus on dimensional reduction, feature extraction, and representation building. We will see how statistical physics, in particular the techniques and concepts of random matrix theory and disordered systems, can be used to understand the properties of these algorithms and the phase transitions taking place in their operation. \par Special attention will be devoted to the socalled highdimensional inference setting, where the numbers of data samples and of defining parameters of the neural nets are comparable. The general principles will be illustrated on recent applications to data coming from neuroscience and genomics, highlighting the potentialities of unsupervised learning for biology. \par Some issues: \\  What is unsupervised learning? \\  Hebbian learning for principal component analysis: retardedlearning phase transition and prior information. \\  Bipartite neural nets and representations: autoencoders, restricted Boltzmann machines, Boltzmann machines. \\  Recurrent neural nets: from point to finitedimensional attractors, temporal sequences. 
Numéro de preprint arXiv  
Commentaires  https://courses.ipht.cnrs.fr/?q=fr/node/197 
Fichiers attachés 
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