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Sparse Gaussian Process Variational Autoencoders
[article]
2020
arXiv
pre-print
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
arXiv:2010.10177v2
fatcat:4slldmmt25gp3mz4gmeevofeq4