[Comp-neuro] CfP Journal Special Issue on Recurrent Neural Networks

Pascal Hitzler hitzler at aifb.uni-karlsruhe.de
Mon Mar 10 15:25:03 CET 2008


Call for Papers: Journal Special Issue on

== Perspectives and Challenges for Recurrent Neural Networks ==

Guest Editors:
Marco Gori, Barbara Hammer, Pascal Hitzler, Guenther Palm

Special issue of the
Elsevier Journal of Algorithms in Cognition, Informatics and Logic
http://www.elsevier.com/wps/find/journaldescription.cws_home/622851/description

= SCOPE =

Recurrent neural networks (RNNs) enable flexible machine learning tools 
which can directly process spatiotemporal and other structured data and 
which offer a rich dynamic repertoire as time dependent systems. They 
promise to be efficient signal-processing models which are biologically 
plausible and optimally suited for a wide range of industrial 
applications on the one hand, and an explanation of cognitive phenomena 
of the human brain on the other hand.

Despite these facts, however, the design of efficient training methods 
for RNNs as well as their mathematical investigation with respect to 
reliable information representation and generalization abilities when 
dealing with complex data structures is still a challenge. It has led to 
diverse approaches and architectures including echo and 
liquid-state-machines, long short term memory, recursive and graph 
networks, core neuro-symbolic integration, etc.  Interestingly, very 
heterogeneous domains are included, such as logic, chaotic systems, and 
biological networks.

The aim of the special issue is to bring together recent work developed 
in the field of recurrent information processing, which bridges the gap 
between different approaches and which sheds some light on canonical 
solutions or principled problems which occur in the context of recursive 
information processing when considered across the disciplines.

= TOPICS =

We particularly encourage submissions connected to the following 
non-exhaustive list of topics:

- new learning paradigms of RNNs such as unsupervised learning or 
reservoire learning
- biologically plausible methods
- integration of RNNs and symbolic reasoning
- universal approaches for general data structures such as sets or graphs
- methods which address the generalization ability of RNNs
- challenging applications which have the potential to be benchmark problems
- visionary papers concerning the future of RNNs

= SUBMISSIONS =

Deadline for submissions is 18th of July, 2008.

Submissions shall follow the guidelines laid out for the Journal of 
Algorithms in Cognition, Informatics and Logic, which can be found under
<http://www.elsevier.com/wps/find/journaldescription.cws_home/622851/authorinstructions>.

Submissions shall be sent as pdf to Pascal Hitzler, 
hitzler at aifb.uni-karlsruhe.de

= EDITORIAL BOARD =

Guilherme da Alencar Barreto, Universidade Federal do Ceara, Brasil
Monica Bianchini, University of Siena, Italy
Howard Blair, Syracuse University, USA
Hendrik Blockeel, KU Leuven, Belgium
Mikael Boden, University of Queensland, Australia
Matthew Cook, ETH Zuerich, Switzerland
Artur d'Avila Garcez, City University London, UK
Luc de Raedt, KU Leuven, Belgium
Steffen Hoelldobler, TU Dresden, Germany
Herbert Jaeger, Jacobs University Bremen, Germany
Stefan C. Kremer, University of Guleph, Canada
Kai-Uwe Kuehnberger, University of Osnabrueck, Germany
Alessio Micheli, University of Pisa, Italy
Barak Pearlmutter, NUI Maynooth, Ireland
Juergen Schmidhuber, TU Munich, Germany
Alessandro Sperduti, University of Padova, Italy
Jochen Steil, University of Bielefeld, Germany
Peter Tino, University of Bermingham, UK
Edmondo Trentin, University of Siena, Italy
Thomas Wennekers, University of Plymouth, UK


This Call for Papers is available online under 
http://www.neural-symbolic.org/RNN_CfP.txt


-- 
PD Dr. Pascal Hitzler
Institute AIFB, University of Karlsruhe, 76128 Karlsruhe
email: hitzler at aifb.uni-karlsruhe.de    fax: +49 721 608 6580
web:   http://www.pascal-hitzler.de   phone: +49 721 608 4751
        http://www.neural-symbolic.org









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