Power quality (PQ) monitoring and detection has emerged as an essential requirement due
to the proliferation of sensitive power electronic interfacing devices, electric vehicle charging …

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 …

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 …

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 …

The Electroencephalogram (EEG) pattern of seizure activities is highly individual-dependent
and requires experienced specialists to annotate seizure events. It is clinically time …

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

Abstract Restricted Boltzmann machines (RBMs) with a binary visible layer of size N and a
Gaussian hidden layer of size P have been proved to be equivalent to a Hopfield neural …

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 …