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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...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2018
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| Materias: | |
| 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 |