New Challenges in Neural Computation (NC2)
Workshop of the
GI-Arbeitskreis Neuronale Netze
and the German Neural Networks Society
in connection to DAGM 2011, Frankfurt
[Home][Call for Papers][Program]
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
support vector machines or
self-organizing maps are well established and readily available in commercial tools,
modern information processing continues to pose challenging tasks to the field.
Not only the amount of data explodes in virtually all application areas but
also their complexity with regard to dimensionality, structural variety, and multimodality.
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.
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.
This year, a special focus topic will center around the
research area 'Autonomous learning and robotics'.
In addition, general contribution to challenges in neural computation are welcome.
A non-exhaustive list of topics tackled in the workshop is given by the following keywords:
neural models for very large data sets, complex learning scenarios and data structures,
principled mathematical models which address scenarios beyond classical classification, regression, clustering, etc.,
principled cognitive paradigms which help to design complex neural systems,
rich representation and relational approaches in statistical machine learning,
suitable application scenarios and evaluation procedures to benchmark
the capability of complex neural systems,
- challenges in robotics and autonomous learning.
We welcome the submission of extended abstracts focussing on novel ideas and
problems in the field.
Submissions should be prepared as pdf files (e.g. using the latex article style)
without page numbering, header, or footer,
and should have one to four pages in length.
Papers should be send to
Barbara Hammer by
e-mail (firstname.lastname@example.org) until
Abstracts of the papers will be published online as
Machine Learning Report.
For further questions, please contact the workshop organizers
- Barbara Hammer, CITEC, Faculty of Technology, Bielefeld University, D-33594 Bielefeld, email@example.com
- Thomas Villmann, Mathematics, University of Applied Sciences Mittweida,
The workshop is sponsored by the
European Neural Networks Society