This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of fini...
We address an approximation method for Gaussian process (GP) regression, where we approximate covariance by a block matrix such that diagonal blocks are calculated exactly while o...
A multitask learning framework is developed for discriminative classification and regression where multiple large-margin linear classifiers are estimated for different predictio...
Given video footage of a person's face, we present new techniques to automatically recover the face position and the facial expression from each frame in the video sequence. ...
Frederic H. Pighin, Richard Szeliski, David Salesi...
Research on how to reason about correctness properties of software systems using model checking is advancing rapidly. Work on exnite-state models from program source code and on ab...
James C. Corbett, Matthew B. Dwyer, John Hatcliff,...