Sparse Gaussian Process Variational Autoencoders
This addresses a computational bottleneck for researchers and practitioners working with large, multi-dimensional spatio-temporal datasets, representing an incremental improvement over existing GP-DGM frameworks.
The paper tackled the computational inefficiency and lack of principled missing data handling in Gaussian process deep generative models by developing the sparse Gaussian process variational autoencoder (SGP-VAE), which outperformed alternative methods like multi-output GPs and structured VAEs in experiments.
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.