Over the last several years, a new probabilistic representation for 3-d volumetric modeling has been developed. The main purpose of the model is to detect deviations from the norm...
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...
Research on mobile robot navigation has produced two major paradigms for mapping indoorenvironments: grid-based and topological. While grid-based methods produce accurate metric m...
We develop the distance dependent Chinese restaurant process (CRP), a flexible class of distributions over partitions that allows for nonexchangeability. This class can be used to...
This work exploits several machine-learning techniques to address the problem of image-quality prediction of synthetic aperture sonar (SAS) imagery. The objective is to predict th...