We use reconfigurable hardware to construct a high throughput Bayesian computing machine (BCM) capable of evaluating probabilistic networks with arbitrary DAG (directed acyclic gr...
We develop a probabilistic modeling framework for multiway arrays. Our framework exploits the link between graphical models and tensor factorization models and it can realize any ...
This paper addresses the protein classification problem, and explores how its accuracy can be improved by using information from time-course gene expression data. The methods are ...
Antonina Mitrofanova, Samantha Kleinberg, Jane Car...
Problem determination in today's computing environments consumes between 30 and 70% of an organization’s IT resources and represents from one third to one half of their tot...
In this paper, we address the problem of automatically detecting and tracking a variable number of persons in complex
scenes using a monocular, potentially moving, uncalibrated ca...
Michael D. Breitenstein, Fabian Reichlin, Bastian ...