This work investigates some of the computational issues involved in the solution of probabilistic reachability problems for discretetime, controlled stochastic hybrid systems. It i...
Alessandro Abate, Saurabh Amin, Maria Prandini, Jo...
While existing mathematical descriptions can accurately account for phenomena at microscopic scales (e.g. molecular dynamics), these are often high-dimensional, stochastic and thei...
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
A new method for visual tracking of articulated objects is presented. Analyzing articulated motion is challenging because the dimensionality increase potentially demands tremendou...