Deep Gaussian Processes with Convolutional Kernels

Understand

Deep Gaussian processes (DGPs) provide a Bayesian non-parametric alternative to standard parametric deep learning models.

• A DGP is formed by stacking multiple GPs resulting in a well-regularized composition of functions.
• The Bayesian framework that equips the model with attractive properties, such as implicit capacity control and predictive uncertainty, makes it at the same time challenging to combine with a convolutional structure.
• This has hindered the application of DGPs in computer vision tasks, an area where deep parametric models (i.e.