Deep learning with differential Gaussian process flows
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Date
2019-04
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Language
en
Pages
16
1-15
1-15
Series
The 22nd International Conference on Artificial Intelligence and Statistic, Volume 89, Proceedings of Machine Learning Research
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 networksDescription
Keywords
gaussian process, Bayesian methods
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Citation
Hegde, P, Heinonen, M, Lähdesmäki, H & Kaski, S 2019, Deep learning with differential Gaussian process flows . in The 22nd International Conference on Artificial Intelligence and Statistic . vol. 89, Proceedings of Machine Learning Research, vol. 89, JMLR, pp. 1-15, International Conference on Artificial Intelligence and Statistics, Naha, Japan, 16/04/2019 . < https://proceedings.mlr.press/v89/hegde19a.html >
