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...
A translation of the Business Process Modeling Notation into the process calculus COWS is presented. The stochastic extension of COWS is then exploited to address quantitative reas...
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...
Abstract--This paper presents a novel and domainindependent approach for graph-based structure learning. The approach is based on solving the Maximum Common SubgraphIsomorphism pro...
This paper is part of a project to match descriptions of real-world instances and probabilistic models, both of which can be described at mulvel of abstraction and detail. We use ...