Deep Gaussian Processes with Importance-Weighted Variational Inference

14 May 2019Hugh SalimbeniVincent DutordoirJames HensmanMarc Peter Deisenroth

Deep Gaussian processes (DGPs) can model complex marginal densities as well as complex mappings. Non-Gaussian marginals are essential for modelling real-world data, and can be generated from the DGP by incorporating uncorrelated variables to the model... (read more)

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