Deep learning with differential Gaussian process flows


Pashupati Hegde, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski ;
Proceedings of Machine Learning Research, 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.

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