Abstract: Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assum...
Many solutions to computer vision and image processing problems involve the minimization of multi-label energy functions with up to K variables in each term. In the minimization pr...
Autonomous cars will likely play an important role in the future. A vision system designed to support outdoor navigation for such vehicles has to deal with large dynamic environme...
We study the problem of visualizing large networks and develop es for effectively abstracting a network and reducing the size to a level that can be clearly viewed. Our size reduc...
We consider the problem of learning a similarity function from a set of positive equivalence constraints, i.e. 'similar' point pairs. We define the similarity in informa...