Bayesian networks are graphical representations of probability distributions. In virtually all of the work on learning these networks, the assumption is that we are presented with...
We present a new method for instance-specific algorithm configuration (ISAC). It is based on the integration of the algorithm configuration system GGA and the recently proposed sto...
Serdar Kadioglu, Yuri Malitsky, Meinolf Sellmann, ...
Abstract. We investigate the extent to which eye movements in natural dynamic scenes can be predicted with a simple model of bottom-up saliency, which learns on different visual re...
Eleonora Vig, Michael Dorr, Thomas Martinetz, Erha...
We propose an algorithm for function approximation that evolves a set of hierarchical piece-wise linear regressors. The algorithm, named HIRE-Lin, follows the iterative rule learn...
Background: In recent years protein structure prediction methods using local structure information have shown promising improvements. The quality of new fold predictions has risen...