The Markov chain approximation method is an effective and widely used approach for computing optimal values and controls for stochastic systems. It was extended to nonlinear (and p...
Abstract— In this paper, we propose a novel, effective and efficient probabilistic pruning criterion for probabilistic similarity queries on uncertain data. Our approach support...
Thomas Bernecker, Tobias Emrich, Hans-Peter Kriege...
The multi-label problem is of fundamental importance to computer vision, yet finding global minima of the associated energies is very hard and usually impossible in practice. Rec...
Evgeny Strekalovskiy, Bastian Goldluecke, Daniel C...
We show that randomization can lead to significant improvements for a few fundamental problems in distributed tracking. Our basis is the count-tracking problem, where there are k...
Binary Decision Diagrams (BDDs) often fail to exploit sharing between Boolean functions that differ only in their support variables. In a memory circuit, for example, the function...