Sparse Gaussian Process Variational Autoencoders

Autor:	Ashman, Matthew, So, Jonathan, Tebbutt, Will, Fortuin, Vincent, Pearce, Michael, Turner, Richard E.
Rok vydání:	2020
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning Computer Science - Neural and Evolutionary Computing
Druh dokumentu:	Working Paper
Popis:	Large, multi-dimensional spatio-temporal datasets are omnipresent in modern science and engineering. An effective framework for handling such data are Gaussian process deep generative models (GP-DGMs), which employ GP priors over the latent variables of DGMs. Existing approaches for performing inference in GP-DGMs do not support sparse GP approximations based on inducing points, which are essential for the computational efficiency of GPs, nor do they handle missing data -- a natural occurrence in many spatio-temporal datasets -- in a principled manner. We address these shortcomings with the development of the sparse Gaussian process variational autoencoder (SGP-VAE), characterised by the use of partial inference networks for parameterising sparse GP approximations. Leveraging the benefits of amortised variational inference, the SGP-VAE enables inference in multi-output sparse GPs on previously unobserved data with no additional training. The SGP-VAE is evaluated in a variety of experiments where it outperforms alternative approaches including multi-output GPs and structured VAEs. Comment: 19 pages, 6 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2010.10177 Zobrazit plný text záznamu View this record from Arxiv