Abstract Machine Learning (ML) is one of the most exciting and dynamic areas of modern
research and application. The purpose of this review is to provide an introduction to the core …

Condensed matter physics (CMP) seeks to understand the microscopic interactions of matter
at the quantum and atomistic levels, and describes how these interactions result in both …

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 …

We consider restricted Boltzmann machine (RBMs) trained over an unstructured dataset
made of blurred copies of definite but unavailable “archetypes” and we show that there …

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 …

Abstract Restricted Boltzmann Machines are simple and powerful generative models that
can encode any complex dataset. Despite all their advantages, in practice the trainings are …

Learning is a distinctive feature of intelligent behaviour. High-throughput experimental data
and Big Data promise to open new windows on complex systems such as cells, the brain or …

The recent progresses in Machine Learning opened the door to actual applications of
learning algorithms but also to new research directions both in the field of Machine Learning …