15 citations · 16 across the 4 of their papers we have counts for
4 papers
Spherical Boltzmann machines: a solvable theory of learning and generation in energy-based models
Thomas Tulinski, Simona Cocco, Rémi Monasson +1
Energy-based models (EBMs) are flexible generative architectures inspired by statistical physics, but their learning and generative properties remain poorly understood. Here, we an…
Replica Theory of Spherical Boltzmann Machine Ensembles
Thomas Tulinski, Jorge Fernandez-De-Cossio-Diaz, Simona Cocco +1
Training in machine learning generally consists in finding one model, whose parameters minimize a data-dependent loss. Yet, empirical work shows that ensemble learning, an approach…
Reconstructing a Random Potential from its Random Walks
Simona Cocco, Remi Monasson
The problem of how many trajectories of a random walker in a potential are needed to reconstruct the values of this potential is studied. We show that this problem can be solved by…
Restart method and exponential acceleration of random 3-SAT instances resolutions: a large deviation analysis of the Davis-Putnam-Loveland-Logemann algorithm
S. Cocco, R. Monasson
The analysis of the solving complexity of random 3-SAT instances using the Davis-Putnam-Loveland-Logemann (DPLL) algorithm slightly below threshold is presented. While finding a so…