15 citations · 21 across the 10 of their papers we have counts for
11 papers
Equilibrium Training of Energy-Based Models with Parallel Trajectory Tempering
Nicolas Béreux, Aurélien Decelle, Cyril Furtlehner +1
Energy-Based Models (EBMs) provide an interpretable framework for generative modeling of scientific data, but poor Markov Chain Monte Carlo mixing often limits their reliability. W…
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…
Inferring effective interactions and task-related brain states from large-scale neural activity with Restricted Boltzmann Machines
Nicolas Béreux, Giovanni Catania, Aurélien Decelle +3
Large-scale electrophysiological recordings now enable the simultaneous monitoring of thousands of neurons across multiple brain regions, revealing structured variability in popula…
PRIVET: PRoximIty leakage detection Via Extreme value Theory
Antoine Szatkownik, Aurélien Decelle, Beatriz Seoane +7
Deep generative models are often trained on sensitive data, such as genetic sequences, health data, or more broadly, any copyrighted, licensed or protected content. This raises cri…
On the role of non-linear latent features in bipartite generative neural networks
Tony Bonnaire, Giovanni Catania, Aurélien Decelle +1
We investigate the phase diagram and memory retrieval capabilities of bipartite energy-based neural networks, namely Restricted Boltzmann Machines (RBMs), as a function of the prio…
A theoretical framework for overfitting in energy-based modeling
Giovanni Catania, Aurélien Decelle, Cyril Furtlehner +1
We investigate the impact of limited data on training pairwise energy-based models for inverse problems aimed at identifying interaction networks. Utilizing the Gaussian model as t…
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…
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…
Cascade of phase transitions in the training of Energy-based models
Dimitrios Bachtis, Giulio Biroli, Aurélien Decelle +1
In this paper, we investigate the feature encoding process in a prototypical energy-based generative model, the Restricted Boltzmann Machine (RBM). We start with an analytical inve…
Inferring effective couplings with Restricted Boltzmann Machines
Aurélien Decelle, Cyril Furtlehner, Alfonso De Jesus Navas Gómez +1
Generative models offer a direct way of modeling complex data. Energy-based models attempt to encode the statistical correlations observed in the data at the level of the Boltzmann…