265 citations · 2.3k across the 48 of their papers we have counts for
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Disentangling representations in Restricted Boltzmann Machines without adversaries
Jorge Fernandez-de-Cossio-Diaz, Simona Cocco, Remi Monasson
A goal of unsupervised machine learning is to build representations of complex high-dimensional data, with simple relations to their properties. Such disentangled representations m…
Optimal regularizations for data generation with probabilistic graphical models
Arnaud Fanthomme, F Rizzato, S Cocco +1
Understanding the role of regularization is a central question in Statistical Inference. Empirically, well-chosen regularization schemes often dramatically improve the quality of t…
Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins
Jérôme Tubiana, Simona Cocco, Rémi Monasson
A Restricted Boltzmann Machine (RBM) is an unsupervised machine-learning bipartite graphical model that jointly learns a probability distribution over data and extracts their relev…