Stochastic tracking of structured models in monolithic state spaces often requires modeling complex distributions that are difficult to represent with either parametric or sample...
Leonid Taycher, John W. Fisher III, Trevor Darrell
Abstract. We describe an efficient approach to construct shape models composed of contour parts with partially-supervised learning. The proposed approach can easily transfer parts ...
Stochastic relational models (SRMs) [15] provide a rich family of choices for learning and predicting dyadic data between two sets of entities. The models generalize matrix factor...
A Bayesian procedure for the simultaneous alignment and classification of sequences into subclasses is described. This Gibbs sampling algorithm iterates between an alignment step ...
We consider the problem of multi-task reinforcement learning where the learner is provided with a set of tasks, for which only a small number of samples can be generated for any g...