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Workshop program

The workshop will take place on 12.9.2016, 9.00 a.m. - 17.50 p.m. accompanying GCPR in Hanover. It will take place at the Hotel Dormero in Hanover.

This year, again, we received a large number of very good contributions. All regular contributions will be presented by a talk of 15 min plus 5 min questions, short contributions will be presented by a talk of 10 min plus 5 min questions.

Preliminary Schedule




Advances in learning vector quantization

  • T. Villmann, M. Kaden, A. Bohnsack: Classification Margin Dependent Exploration Horizons of Prototypes for Outlier Robust Classification in Learning Vector Quantization
  • B. Paassen, A. Schulz, B. Hammer: Linear Supervised Transfer Learning for Generalized Matrix LVQ
  • K. Bunte, E. S. Baranowski, W. Arlt, P. Tino: Relevance Learning Vector Quantization in Variable Dimensional Spaces


Processing time series data

  • F. Melchert, U. Seiffert, M. Biehl: Functional approximation for the classification of smooth time series
  • W. Aswolinskiy, J. Steil: Parameterized Pattern Generation via Regression in the Model Space of Echo State Networks
  • F. Raue, M. Liwicki, A. Dengel: Symbolic Association Learning inspired by the Symbol Grounding Problem


Coffee break


Keynote talk
Marc Toussaint (University of Stuttgart): Representation Learning - I've heard that one before


Lunch break


Keynote talk
Jörg Lücke (University of Oldenburg): Neural Simpletrons - Minimalistic Deep Neural Networks for Probabilistic Learning with Few Labels


Sampling, modelling, and optimization

  • O. Walter, R Häb-Umbach: Unsupervised Word Discovery from Speech using Bayesian Hierarchical Models
  • R. Rayyes, J. Steil: Goal Babbling with Direction Sampling for simultaneous exploration and learning of inverse kinematics of a humanoid robot
  • J. Brinkrolf, T. Mittag, R. Joppen, A. Dröge, K.-H. Pietsch, B. Hammer: Virtual optimisation for improved production planning


Coffee break


Computer vision and deep learning

  • H. Berntsen, W. Kuijper, T. Heskes: The Artificial Mind's Eye - Resisting Adversarials for Convolutional Neural Networks using Internal Projection
  • M. Garbade, J. Gall: Handcrafting vs Deep Learning: An Evaluation of NTraj+ Features for Pose Based Action Recognition
  • J. Kreger, L. Fischer, U. Bauer-Wersing, T. Weisswange: Quality Prediction for a Road Detection System
  • P. P. Fouopi, G. Srinivas, S. Knake-Langhorst, F. Köster: Object Detection Based on Deep Learning and Context Information


Nomination of the best presentation award, closing


Meeting of the GI Fachgruppe Neural Networks