Bayesian approaches to supervised learning use priors on the classifier parameters. However, few priors aim at achieving "sparse" classifiers, where irrelevant/redundant...
The human figure exhibits complex and rich dynamic behavior that is both nonlinear and time-varying. However, most work on tracking and synthesizing figure motion has employed eit...
Vladimir Pavlovic, James M. Rehg, Tat-Jen Cham, Ke...
We developed a model based on nonparametric Bayesian modeling for automatic discovery of semantic relationships between words taken from a corpus. It is aimed at discovering seman...
Abstract. This paper presents a new Bayesian approach to the problem of finding correspondences of moving objects in a multiple calibrated camera environment. Moving objects are d...
Cristian Canton-Ferrer, Josep R. Casas, Montse Par...
We introduce a new model for extracting classified structural segments, such as intro, verse, chorus, break and so forth, from recorded music. Our approach is to classify signal ...
Samer A. Abdallah, Katy Noland, Mark B. Sandler, M...