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 …

The Hopfield model and the Boltzmann machine are among the most popular examples of
neural networks. The latter, widely used for classification and feature detection, is able to …

Generative modeling, which learns joint probability distribution from data and generates
samples according to it, is an important task in machine learning and artificial intelligence …

In this book, we provide a comprehensive introduction to the most recent advances in the
application of machine learning methods in quantum sciences. We cover the use of deep …

This article is an extended version of previous work of Lesieur et al (2015 IEEE Int. Symp. on
Information Theory Proc. pp 1635–9 and 2015 53rd Annual Allerton Conf. on …

Motivated by recent progress in using restricted Boltzmann machines as preprocessing
algorithms for deep neural network, we revisit the mean-field equations [belief-propagation …

Abstract 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 …

Data sets in the real world are often complex and to some degree hierarchical, with groups
and subgroups of data sharing common characteristics at different levels of abstraction …

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 …

The goal of this book is to present new mathematical techniques for studying the behaviour
of mean-field systems with disordered interactions. We mostly focus on certain problems of …