Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …

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 …

Motivated by the close relations of the renormalization group with both the holography
duality and the deep learning, we propose that the holographic geometry can emerge from …

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 …

Restricted Boltzmann machines (RBM) are bilayer neural networks used for the
unsupervised learning of model distributions from data. The bipartite architecture of RBM …

Abstract Machine learning algorithms relying on deep neural networks recently allowed a
great leap forward in artificial intelligence. Despite the popularity of their applications, the …