128 citations · 232 across the 6 of their papers we have counts for
3 papers · 1 filter
'Place-cell' emergence and learning of invariant data with restricted Boltzmann machines: breaking and dynamical restoration of continuous symmetries in the weight space
Moshir Harsh, Jérôme Tubiana, Simona Cocco +1
Distributions of data or sensory stimuli often enjoy underlying invariances. How and to what extent those symmetries are captured by unsupervised learning methods is a relevant que…
Inference of compressed Potts graphical models
Francesca Rizzato, Alice Coucke, Eleonora de Leonardis +4
We consider the problem of inferring a graphical Potts model on a population of variables, with a non-uniform number of Potts colors (symbols) across variables. This inverse Potts…
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…