From the 1 of 12 linked papers with an AI index.
12 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…
The Symmetric Perceptron: a Teacher-Student Scenario
Giovanni Catania, Aurélien Decelle, Suhanee Korpe
We introduce and solve a teacher-student formulation of the symmetric binary Perceptron, turning a traditionally storage-oriented model into a planted inference problem with a guar…
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…
Predicting large scale cosmological structure evolution with generative adversarial network-based autoencoders
Marion Ullmo, Nabila Aghanim, Aurélien Decelle +1
Predicting the nonlinear evolution of cosmic structure from initial conditions is typically approached using Lagrangian, particle-based methods. These techniques excel in terms of…