Making the coupled gaussian process dynamical model modular and scalable with variational approximations

HIGHLIGHTS

• who: Dmytro Velychko and collaborators from the Department of Psychology, University of Marburg, Gutenbergstr18, Marburg, Germany have published the research: Making the Coupled Gaussian Process Dynamical Model Modular and Scalable with Variational Approximations, in the Journal: Entropy 2018, 20, 724 of /2018/
• what: The aim of the approximation is threefold: first to reduce training time of the model; second to enable modular re-use of learned dynamics; and third to store these learned dynamics compactly. The authors show that the CGPDM outperforms several other MP models on movement trajectory prediction. For this paper, an MP . . .
  

   

  

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