Doubly Stochastic Variational Inference for Deep Gaussian Processes

NeurIPS 2017 Hugh SalimbeniMarc Deisenroth

Gaussian processes (GPs) are a good choice for function approximation as they are flexible, robust to over-fitting, and provide well-calibrated predictive uncertainty. Deep Gaussian processes (DGPs) are multi-layer generalisations of GPs, but inference in these models has proved challenging... (read more)

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