Despite outstanding successes of the state-of-the-art clustering algorithms, many of them still suffer from shortcomings. Mainly, these algorithms do not capture coherency and homo...
We present a system for taxonomy extraction, aimed at providing a taxonomic backbone in an ontology learning environment. We follow previous research in using hierarchical clusteri...
We consider the problem of multi-task reinforcement learning, where the agent needs to solve a sequence of Markov Decision Processes (MDPs) chosen randomly from a fixed but unknow...
Aaron Wilson, Alan Fern, Soumya Ray, Prasad Tadepa...
The following article presents a novel, adaptive initialization scheme that can be applied to most state-of-the-art Speaker Diarization algorithms, i.e. algorithms that use agglom...
We present a Bayesian blackboard system for temporal perception, applied to a minidomain task in musical scene analysis. It is similar to the classic Copycat architecture (Hofstad...