Probabilistic mixture models are used for a broad range of data analysis tasks such as clustering, classification, predictive modeling, etc. Due to their inherent probabilistic na...
Probabilistic model building methods can render difficult problems feasible by identifying and exploiting dependencies. They build a probabilistic model from the statistical prope...
We present a probabilistic, salience-based approach to the interpretation of pointing gestures together with spoken utterances. Our mechanism models dependencies between spatial a...
We present a technique that enables the use of finite model finding to check the satisfiability of certain formulas whose intended models are infinite. Such formulas arise when us...
We present a probabilistic formulation of UCS (a sUpervised Classifier System). UCS is shown to be a special case of mixture of experts where the experts are learned independentl...
Narayanan Unny Edakunni, Tim Kovacs, Gavin Brown, ...