The field of stochastic optimization studies decision making under uncertainty, when only probabilistic information about the future is available. Finding approximate solutions to...
It is shown here that stability of the stochastic approximation algorithm is implied by the asymptotic stability of the origin for an associated ODE. This in turn implies convergen...
Abstract. We present a technique for learning the parameters of a continuousstate Markov random field (MRF) model of optical flow, by minimizing the training loss for a set of grou...
We present a new fast implementation of a non-rigid registration algorithm, based on a finite element elastic deformation model using the mutual information metric with a linear e...
Most algorithms for computing diagnoses within a modelbased diagnosis framework are deterministic. Such algorithms guarantee soundness and completeness, but are NPhard. To overcom...
Alexander Feldman, Gregory M. Provan, Arjan J. C. ...