Sparse Gaussian Process Variational Autoencoders

• URL: http://arxiv.org/abs/2010.10177v2
• Date: Fri, 23 Oct 2020 10:29:06 GMT
• Title: Sparse Gaussian Process Variational Autoencoders
• Authors: Matthew Ashman, Jonathan So, Will Tebbutt, Vincent Fortuin, Michael Pearce, Richard E. Turner
• Abstract summary: Existing approaches for performing inference in GP-DGMs do not support sparse GP approximations based on points. We develop the sparse Gaussian processal variation autoencoder (GP-VAE) characterised by the use of partial inference networks for parameterising sparse GP approximations.
• Score: 24.86751422740643
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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