You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

This paper deals with the use of emerging deep learning techniques in future wireless
communication networks. It will be shown that the data-driven approaches should not …

In spite of remarkable progress in deep latent variable generative modeling, training still
remains a challenge due to a combination of optimization and generalization issues. In …

Deep learning based on artificial neural networks (ANNs) is a powerful machine-learning
method that, in recent years, has been successfully used to realize tasks such as image …

Deep learning methods typically require vast amounts of training data to reach their full
potential. While some publicly available datasets exists, domain specific data always needs …

Anomaly detection methods are used to find abnormal states, instances or data points that
differ from a normal sample from the data domain space. Industrial processes are a domain …

Common approaches to control a data-center cooling system rely on approximated
system/environment models that are built upon the knowledge of mechanical cooling and …

The performance of a neural network depends on the availability of datasets, and most deep
learning techniques lack accuracy and generalization when they are trained using limited …

In this letter, we deal with the reality gap from a novel perspective, targeting transferring
deep reinforcement learning (DRL) policies learned in simulated environments to the real …

A key source of brittleness for robotic systems is the presence of model uncertainty and
external disturbances. Most existing approaches to robust control either seek to bound the …