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...
We analyse the computational complexity of phonological models as they have developed over the past twenty years. The major results ate that generation and recognition are undecid...
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 ...