Coupling Gaussian Process Dynamical Models with Product-of-Experts Kernels

  • Dmytro Velychko
  • Dominik Endres
  • Nick Taubert
  • Martin A. Giese
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8681)


We describe a new probabilistic model for learning of coupled dynamical systems in latent state spaces. The coupling is achieved by combining predictions from several Gaussian process dynamical models in a product-of-experts fashion. Our approach facilitates modulation of coupling strengths without the need for computationally expensive re-learning of the dynamical models. We demonstrate the effectiveness of the new coupling model on synthetic toy examples and on high-dimensional human walking motion capture data.


Gaussian Process Products of Experts Computer Graphics 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Dmytro Velychko
    • 1
    • 2
  • Dominik Endres
    • 1
    • 2
  • Nick Taubert
    • 1
  • Martin A. Giese
    • 1
  1. 1.Section Computational Sensomotorics, Department of Cognitive NeurologyUniversity Clinic Tübingen, CIN, HIH and University of TübingenTübingenGermany
  2. 2.Theoretical Neuroscience, Dept. of PsychologyPhilipps-University MarburgMarburgGermany

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