During the last decades, the research community of medical imaging has witnessed
continuous advances in image registration methods, which pushed the limits of the state-of …

Thanks to their inherent properties, probabilistic graphical models are one of the prime
candidates for machine learning and decision making tasks especially in uncertain domains …

INTRODUCTION Compositionality, generalization, and learning from a few examples are
among the hallmarks of human intelligence. CAPTCHAs (Completely Automated Public …

Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that
arise in quantum physics and random matrix theory. In contrast to traditional structured …

A general framework for constructing and using probabilistic models of complex systems that
would enable a computer to use available information for making decisions. Most tasks …

A fundamental challenge in developing high-impact machine learning technologies is
balancing the need to model rich, structured domains with the ability to scale to big data …

The second edition of this bestseller provides a practical and accessible introduction to the
main concepts, foundation, and applications of Bayesian networks. This edition contains a …

Handbook of Knowledge Representation describes the essential foundations of Knowledge
Representation, which lies at the core of Artificial Intelligence (AI). The book provides an up …

Powerful statistical models that can be learned efficiently from large amounts of data are
currently revolutionizing computer vision. These models possess a rich internal structure …

As artificial intelligence is being increasingly used for high-stakes applications, it is
becoming more and more important that the models used be interpretable. Bayesian …