Sparse Gaussian Process Variational Autoencoders

Matthew Ashman, Jonathan So, William Tebbutt, Vincent Fortuin, Michael Arthur Leopold Pearce, Richard E Turner

Keywords: Gaussian process, variational inference, variational autoencoders, Bayesian inference

Abstract: 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.

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Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2010.10177/code)

Reviewed Version (pdf): https://openreview.net/references/pdf?id=CAHI7_k3OD