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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Detalles Bibliográficos
Autores principales: Velychko, Dmytro, Knopp, Benjamin, Endres, Dominik
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512289/
https://www.ncbi.nlm.nih.gov/pubmed/33265813
http://dx.doi.org/10.3390/e20100724

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512289/
https://www.ncbi.nlm.nih.gov/pubmed/33265813
http://dx.doi.org/10.3390/e20100724

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