In these Lecture Notes, we provide a comprehensive introduction to the most recent
advances in the application of machine learning methods in quantum sciences. We cover …

Generative models emerge as promising candidates for novel sequence-data driven
approaches to protein design, and for the extraction of structural and functional information …

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 …

A goal of unsupervised machine learning is to build representations of complex high-
dimensional data, with simple relations to their properties. Such disentangled …

We revisit the challenging problem of training Gaussian-Bernoulli restricted Boltzmann
machines (GRBMs), introducing two innovations. We propose a novel Gibbs-Langevin …

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 …

Applications of generative models for genomic data have gained significant momentum in
the past few years, with scopes ranging from data characterization to generation of genomic …

To improve the storage capacity of the Hopfield model, we develop a version of the
dreaming algorithm that perpetually reinforces the patterns to be stored (as in the Hebb …