|Markus Kaiser 933db3d9d2|
This paper summarizes a way of formulating priors for multiple functions using GPs. The covariance structure between these processes is assumed to be governed by convolutions over latent processes with non-linear alignment of the different outputs. Both the alignment functions and a warping after the convolutions are learned in the context of a deep GP.
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