he International Conference on Artificial Neural Networks (ICANN)
is the annual flagship conference of the European Neural Network
Society (ENNS). In 2014 the University of Hamburg will organize
the 24th ICANN Conference from 15th to 19th September 2014 in
CHAIRS and ORGANISATION:
Stefan Wermter (Hamburg, Germany)
Alessandro E.P. Villa (Lausanne, Switzerland, ENNS President)
Wlodzislaw Duch (Torun, Poland & Singapore, ENNS Past-President)
Petia Koprinkova-Hristova (Sofia, Bulgaria)
Günther Palm (Ulm, Germany)
Cornelius Weber (Hamburg, Germany)
Timo Honkela (Helsinki, Finland)
Local Organizing Committee Chairs:
Sven Magg, Johannes Bauer, Jorge Chacon, Stefan Heinrich, Doreen
Jirak, Katja Koesters, Erik Strahl
Christopher M. Bishop (Microsoft Research, Cambridge, UK)
Yann LeCun (New York University, NY, USA)
Kevin Gurney (University of Sheffield, Sheffield, UK)
Barbara Hammer (Bielefeld University, Bielefeld, Germany)
Jun Tani (KAIST, Daejeon, Republic of Korea)
Paul Verschure (Universitat Pompeu Fabra, Barcelona, Spain)
Hamburg is the second-largest city in Germany, home to over
1.8 million people. Situated at the river Elbe, the port of Hamburg
is the second-largest port in Europe. The University of Hamburg is
the largest institution for research and education in the north of
The venue of the conference is the ESA building of the University
of Hamburg, situated at Edmund-Siemers-Allee near the city centre
and easily reachable from Dammtor Railway Station. Hamburg Airport
can be reached easily via public transport.
For the accomodation we arranged guaranteed rates for a couple of
hotels in Hamburg for ICANN 2014.
ICANN 2014 will feature the main tracks Brain Inspired Computing and
Machine Learning research, with strong cross-disciplinary
interactions and applications. All research fields dealing with
Neural Networks will be present at the conference.
A non-exhaustive list of topics includes:
Brain Inspired Computing: Cognitive models, Computational
Neuroscience, Self-organization, Reinforcement Learning, Neural
Control and Planning, Hybrid Neural-Symbolic Architectures,
Neural Dynamics, Recurrent Networks, Deep Learning.
Machine Learning: Neural Network Theory, Neural Network Models,
Graphical Models, Bayesian Networks, Kernel Methods, Generative
Models, Information Theoretic Learning, Reinforcement Learning,
Relational Learning, Dynamical Models.
Neural Applications for: Intelligent Robotics, Neurorobotics,
Language Processing, Image Processing, Sensor Fusion, Pattern
Recognition, Data Mining, Neural Agents, Brain-Computer
Interaction, Neural Hardware, Evolutionary Neural Networks.