From the 1 of 10 linked papers with an AI index.
10 papers
Equilibrium Training of Energy-Based Models with Parallel Trajectory Tempering
Nicolas Béreux, Aurélien Decelle, Cyril Furtlehner +1
The paper proposes Parallel Trajectory Tempering (PTT), a training method that keeps equilibrium sampling throughout learning of energy‑based models, enabling fast and stable train…
A solvable model for unsupervised federated learning
Giovanni Catania, Aurélien Decelle, Gianluca Manzan +2
We introduce a theoretical framework for analyzing federated learning in a generative setting through a teacher-multiple interacting students scenario, in which each student receiv…
Distributional simplicity bias and effective convexity in Energy Based Models
Aurélien Decelle, Alfonso de Jesús Navas Gómez, Beatriz Seoane
Energy-based learning is a powerful framework for generative modelling, but its training is inherently non-convex, leading potentially to sensitivity to initialisation, poor local…
Uncovering statistical structure in large-scale neural activity with Restricted Boltzmann Machines
Nicolas Béreux, Giovanni Catania, Aurélien Decelle +3
Large-scale electrophysiological recordings now allow simultaneous monitoring of thousands of neurons across multiple brain regions, revealing structured variability in neural popu…
Fast training and sampling of Restricted Boltzmann Machines
Nicolas Béreux, Aurélien Decelle, Cyril Furtlehner +2
Restricted Boltzmann Machines (RBMs) are powerful tools for modeling complex systems and extracting insights from data, but their training is hindered by the slow mixing of Markov…
Inferring Higher-Order Couplings with Neural Networks
Aurélien Decelle, Alfonso de Jesús Navas Gómez, Beatriz Seoane
Maximum entropy methods, rooted in the inverse Ising/Potts problem from statistical physics, are widely used to model pairwise interactions in complex systems across disciplines su…