We present a novel technique for automated problem decomposition to address the problem of scalability in reinforcement learning. Our technique makes use of a set of near-optimal ...
Peng Zang, Peng Zhou, David Minnen, Charles Lee Is...
This paper discusses non-parametric regression between Riemannian manifolds. This learning problem arises frequently in many application areas ranging from signal processing, comp...
This paper reviews the literature linking information and communications technology (ICT) to teaching thinking skills and advocates a dialogic framework which has implications for ...
A paradigm for music expression understanding based on a joint semantic space, described by both affective and sensorial adjectives, is presented. Machine learning techniques were...
Abstract In this paper we question the problem of a software engineering researcher, who in his daily work, has to deal with researching, teaching and learning activities at the sa...