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...
Variational inference methods, including mean field methods and loopy belief propagation, have been widely used for approximate probabilistic inference in graphical models. While ...
—This paper presents a novel approach for automatic recognition of human activities from video sequences. We first group features with high correlations into Category Feature Vec...
This paper presents a kernel density estimation method by means of real-coded crossovers. Estimation of density algorithms (EDAs) are evolutionary optimization techniques, which d...
We study the properties of the Minimum Description Length principle for sequence prediction, considering a two-part MDL estimator which is chosen from a countable class of models....