Deep learning with differential Gaussian process flows
Machine Learning
2018-10-16 v2 Machine Learning
Abstract
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 state-of-the-art results that exceed the performance of deep Gaussian processes and neural networks
Keywords
Cite
@article{arxiv.1810.04066,
title = {Deep learning with differential Gaussian process flows},
author = {Pashupati Hegde and Markus Heinonen and Harri Lähdesmäki and Samuel Kaski},
journal= {arXiv preprint arXiv:1810.04066},
year = {2018}
}