Bayesian networks are an attractive modeling tool for human sensing, as they combine an intuitive graphical representation with ef?cient algorithms for inference and learning. Ear...
Tanzeem Choudhury, James M. Rehg, Vladimir Pavlovi...
Abstract. In this paper we address the problem of finding gene regulatory networks from experimental DNA microarray data. Different approaches to infer the dependencies of gene r...
Christian Spieth, Felix Streichert, Nora Speer, An...
In network performance tomography, characteristics of the network interior are inferred by correlating end-to-end measurements. In much previous work, the presence of correlations...
Bayesian networks provide a modeling language and associated inference algorithm for stochastic domains. They have been successfully applied in a variety of medium-scale applicati...
A monocular vision based location algorithm is presented to detect and track rear vehicles for lane change assist. The algorithm uses the shadow underneath the vehicle to extract ...
Wei Liu, Chunyan Song, Pengyu Fu, Nan Wang, Huai Y...