A novel framework for spatially estimating unknown image data is presented. Common applications include inpainting, concealment of transmission errors, prediction in video coding,...
Haricharan Lakshman, Patrick Ndjiki-Nya, Martin K&...
We extend our previous work on Multilinear Independent Component Analysis (MICA) by introducing a Fast-MICA algorithm that demonstrates the same improvement over classical ICA as ...
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 investigates the combination of different neural network topologies for probabilistic feature extraction. On one hand, a five-layer neural network used in bottle neck f...
An extension of principal component analysis called ipPCA has been proposed earlier for analyzing structure in genetic data. This non-parametric framework iteratively classifies ...