New Challenges in Neural Computation (NC2)

Workshop of the GI-Arbeitskreis Neuronale Netze at KI 2010 Karlsruhe

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Neural computation and biologically inspired data processing systems constitute essential topics in artificial intelligence accompanied by a well established theoretical foundation and numerous successful applications in science and industry. Although some of the most popular methods such as learning for multilayer perceptrons, associative memories, or self-organizing maps are well established and readily available in commercial tools, modern information processing continues to pose challenging tasks to the field which are far from being solved. Not only the amount of data explodes in virtually all application areas but also their complexity with regard to dimensionality, structural variety, and multimodality, such that models have to deal with very large and heterogeneous data sets. At the same time, the tasks become more and more complex, moving from simple classification or prediction in pattern recognition to involved learning scenarios in dynamic environments with no explicit single objective, such that models can no longer be based on simple error measures. Humans are capable of handling complex situations and tasks by means of a combination of different paradigms, whereas existing neural systems mostly mirror only one or a few facets of the whole game.

The goal of the workshop is to figure out paradigms, concepts, and models to extend neural systems to complex situations and tasks and to identify good benchmark scenarios in which to test advanced capacities of model systems. Concrete problems tackled in the workshop include the following:


Submissions should be prepared as pdf files and should not exceed 8 pages. Final manuscripts should be according to the LNCS style as specified in the instructions on the KI call for papers. Papers should be send to Barbara Hammer by e-mail ( until 21.6.2010.


Abstracts of the papers will be available at the KI-workshop. Further, accepted submissions will be published as Machine Learning Report.


For further questions, please contact the workshop organizers

Barbara Hammer