Abstract. We propose a novel unsupervised transfer learning framework that utilises unlabelled auxiliary data to quantify and select the most relevant transferrable knowledge for r...
We introduce a graphical framework for Bayesian inference that is sufficiently general to accommodate not just the standard case but also recent proposals for a theory of quantum...
Abstract--The capacity for realtime synchronization and coordination is a common ability among trained musicians performing a music score that presents an interesting challenge for...
Abstract. We propose an algorithmic framework for computing global solutions of variational models with convex regularity terms that permit quite arbitrary data terms. While the mi...
Thomas Pock, Daniel Cremers, Horst Bischof, Antoni...
Abstract--Over the past three decades we have steadily increased our knowledge on the genetic basis of many severe disorders. Nevertheless, there are still great challenges in appl...
Yaniv Erlich, Assaf Gordon, Michael Brand, Gregory...