Counterexample-guided abstraction refinement (CEGAR) has been en vogue for the automatic verification of very large systems in the past years. When trying to apply CEGAR to the ver...
A probabilistic scheduling model for software projects is presented. The model explicitly takes a scheduling strategy as input. When the scheduling strategy is fixed, the model ou...
This paper applies the Mixture of Gaussians probabilistic model, combined with Expectation Maximization optimization to the task of summarizing three dimensional range data for a ...
Matthew M. Williamson, Roderick Murray-Smith, Volk...
We propose a non-linear graphical model for structured prediction. It combines the power of deep neural networks to extract high level features with the graphical framework of Mar...
A new probabilistic image segmentation model based on hypothesis testing and Gibbs Random Fields is introduced. First, a probabilistic difference measure derived from a set of hyp...