79 citations · 449 across the 30 of their papers we have counts for
31 papers
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…
Learning a Restricted Boltzmann Machine using biased Monte Carlo sampling
Nicolas Béreux, Aurélien Decelle, Cyril Furtlehner +1
Restricted Boltzmann Machines are simple and powerful generative models that can encode any complex dataset. Despite all their advantages, in practice the trainings are often unsta…
Equilibrium and non-Equilibrium regimes in the learning of Restricted Boltzmann Machines
Aurélien Decelle, Cyril Furtlehner, Beatriz Seoane
Training Restricted Boltzmann Machines (RBMs) has been challenging for a long time due to the difficulty of computing precisely the log-likelihood gradient. Over the past decades,…
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…
Robust Multi-Output Learning with Highly Incomplete Data via Restricted Boltzmann Machines
Giancarlo Fissore, Aurélien Decelle, Cyril Furtlehner +1
In a standard multi-output classification scenario, both features and labels of training data are partially observed. This challenging issue is widely witnessed due to sensor or da…
Gaussian-Spherical Restricted Boltzmann Machines
Aurélien Decelle, Cyril Furtlehner
We consider a special type of Restricted Boltzmann machine (RBM), namely a Gaussian-spherical RBM where the visible units have Gaussian priors while the vector of hidden variables…