Publications (39)
Using Latent Binary Variables for Online Reconstruction of Large Scale Systems
Victorin Martin, Jean-Marc Lasgouttes, Cyril Furtlehner
We propose a probabilistic graphical model realizing a minimal encoding of real variables dependencies based on possibly incomplete observation and an empirical cumulative distribu…
Pairwise MRF Calibration by Perturbation of the Bethe Reference Point
Cyril Furtlehner, Yufei Han, Jean-Marc Lasgouttes +1
We investigate different ways of generating approximate solutions to the pairwise Markov random field (MRF) selection problem. We focus mainly on the inverse Ising problem, but dis…
Restricted Boltzmann Machine, recent advances and mean-field theory
Aurélien Decelle, Cyril Furtlehner
This review deals with Restricted Boltzmann Machine (RBM) under the light of statistical physics. The RBM is a classical family of Machine learning (ML) models which played a centr…
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…
Free Dynamics of Feature Learning Processes
Cyril Furtlehner
Regression models usually tend to recover a noisy signal in the form of a combination of regressors, also called features in machine learning, themselves being the result of a lear…
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…