Which one comes first: segmentation or recognition? We propose a unified framework for carrying out the two simultaneously and without supervision. The framework combines a fle...
We present algorithms for parallel probabilistic model checking on general purpose graphic processing units (GPGPUs). Our improvements target the numerical components of the tradit...
Dragan Bosnacki, Stefan Edelkamp, Damian Sulewski,...
This paper presents a novel method for learning object manipulation such as rotating an object or placing one object on another. In this method, motions are learned using referenc...
Cognitive Agents must be able to decide their actions based on their recognized states. In general, learning mechanisms are equipped for such agents in order to realize intellgent ...
The work presented in this paper explores a supervised method for learning a probabilistic model of a lexicon of VerbNet classes. We intend for the probabilistic model to provide ...
-- Most optimization algorithms that use probabilistic models focus on extracting the information from good solutions found in the population. A selection method discards the below...
Adaptive techniques like voltage and frequency scaling, process variations and the randomness of input data contribute signi cantly to the statistical aspect of contemporary hardwa...
In this paper we consider how to efficiently identify tags on the moving conveyor. Considering conditions like the path loss and multi-path effect in realistic settings, we first p...
Lei Xie, Bo Sheng, Chiu Chiang Tan, Hao Han, Qun L...
In this paper, we consider the problem of community detection in directed networks by using probabilistic models. Most existing probabilistic models for community detection are ei...
The Maximal Covering Location Problem (MCLP) maximizes the population that has a facility within a maximum travel distance or time. Numerous extensions have been proposed to enhan...