2 citations · 4 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
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…
PRIVET: Privacy Metric Based on Extreme Value Theory
Antoine Szatkownik, Aurélien Decelle, Beatriz Seoane +6
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…
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…
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…