Deep learning with differential Gaussian process flows
Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics, PMLR 89:1812-1821, 2019.
We propose a novel deep learning paradigm of differential flows that learn a stochastic differential equation transformations of inputs prior to a standard classification or regression function. The key property of differential Gaussian processes is the warping of inputs through infinitely deep, but infinitesimal, differential fields, that generalise discrete layers into a dynamical system. We demonstrate excellent results as compared to deep Gaussian processes and Bayesian neural networks.