Making the Coupled Gaussian Process Dynamical Model Modular and Scalable with Variational Approximations

Making the Coupled Gaussian Process Dynamical Model Modular and Scalable with Variational Approximations

We describe a sparse, variational posterior approximation to the Coupled Gaussian Process Dynamical Model (CGPDM), which is a latent space coupled dynamical model in discrete time. The purpose of the approximation is threefold: first, to reduce training time of the model; second, to enable modular r...

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Bibliographic Details
Main Authors: Dmytro Velychko, Benjamin Knopp, Dominik Endres
Format: Article
Language:English
Published: MDPI AG 2018-09-01
Series:Entropy
Subjects:
Gaussian processes
variational methods
movement primitives
modularity
Online Access:http://www.mdpi.com/1099-4300/20/10/724

Internet

http://www.mdpi.com/1099-4300/20/10/724

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