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Welcome to ICANN 2019

17th – 19th September, 2019, Munich, Germany

The 28th International Conference on Artificial Neural Networks.
A conference of the European Neural Network Society.



Conference proceedings are published by Springer in Lecture Notes in Computer Science.

The International Conference on Artificial Neural Networks (ICANN) is the annual flagship conference of the European Neural Network Society (ENNS). The ideal of ICANN is to bring together researchers from two worlds: information sciences and neurosciences. The scope is wide, ranging from machine learning algorithms to models of real nervous systems. The aim is to facilitate discussions and interactions in the effort towards developing more intelligent computational systems and increasing our understanding of neural and cognitive processes in the brain.

Conference topics

ICANN 2019 will feature two 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

Deep Learning

  • Neural Network Theory
  • Neural Network Models
  • Graphical Models
  • Bayesian Networks
  • Kernel Methods
  • Generative Models
  • Information Theoretic Learning
  • Reinforcement Learning
  • Relational Learning
  • Dynamical Models
  • Recurrent Networks.

Brain Inspired Computing

  • Cognitive models
  • Computational Neuroscience
  • Self-organisation
  • Reinforcement Learning
  • Neural Control and Planning
  • Hybrid Neural-Symbolic Architectures
  • Neural Dynamics

Neural Applications for

  • Bioinformatics
  • Biomedicine
  • Intelligent Robotics
  • Neurorobotics
  • Language Processing
  • Image Processing
  • Sensor Fusion
  • Pattern Recognition
  • Data Mining
  • Neural Agents
  • Brain-Computer Interaction
  • Neural Hardware
  • Evolutionary Neural Networks

Special session (BIGCHEM)

  • Big Data analysis in chemistry
  • Chemoinformatics
  • Use of deep learning to predict molecular properties
  • Drug-discovery
  • Modelling and prediction of chemical reaction data
  • Synthetic route prediction
  • Structure generation
  • Molecular dynamics simulations and quantum chemistry


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