Constructing tractable dependent probability distributions over structured continuous random vectors is a central problem in statistics and machine learning. It has proven diffic...
We accelerate state space exploration for explicit-state model checking by executing complex operations on the graphics processing unit (GPU). In contrast to existing approaches en...
Traditional binary hypothesis testing relies on the precise knowledge of the probability density of an observed random vector conditioned on each hypothesis. However, for many app...
This paper addresses the problem of interactively modeling large street networks. We introduce a modeling framework that uses tensor fields to guide the generation of a street gra...
Guoning Chen, Gregory Esch, Peter Wonka, Pascal M&...
Reasoning about graph and model transformation systems is an important means to underpin model-driven software engineering, such as Model-Driven Architecture (MDA) and Model Integ...