Abstract. The reconstruction of a complex scene from multiple images is a fundamental problem in the field of computer vision. Volumetric methods have proven to be a strong altern...
Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data. We extend stru...
Causality constraints of random discrete simulation make parallel and distributed processing difficult. Methods of applying reconfigurable logic to implement and accelerate simula...
We address the problem of learning structure in nonlinear Markov networks with continuous variables. This can be viewed as non-Gaussian multidimensional density estimation exploit...
We study the biochemical processes involved in scaffold-mediated crosstalk between the cAMP and the Raf-1/MEK/ERK pathways. We model the system by a continuous time Markov chain w...