Abstract— The future of robots, as our companions is dependent on their ability to understand, interpret and represent the environment in a human compatible manner. Towards this ...
A hybrid Bayesian Network (BN) is one that incorporates both discrete and continuous nodes. In our extensive applications of BNs for system dependability assessment the models are...
Martin Neil, Manesh Tailor, Norman E. Fenton, Davi...
This paper addresses exact learning of Bayesian network structure from data and expert's knowledge based on score functions that are decomposable. First, it describes useful ...
We develop a theory for the temporal integration of visual motion motivated by psychophysical experiments. The theory proposes that input data are temporally grouped and used to p...
Alan L. Yuille, Pierre-Yves Burgi, Norberto M. Grz...
Abstract. Massive real-world data are network-structured, such as social network, relationship between proteins and power grid. Discovering the latent communities is a useful way f...