Bayesian Basis Function Approximation for Scalable Gaussian Process Priors in Deep Generative Models

A scalable basis function approximation technique for GP prior VAEs that mitigates challenges and results in linear time complexity, with a global parametrization that eliminates the need for amortized variational inference and the associated amortization gap, making it well-suited for conditional generation tasks where accuracy and efficiency are crucial.