Deep learning with differential Gaussian process flows

Pashupati Hegde; Markus Heinonen; Harri Lähdesmäki; Samuel Kaski

Deep learning with differential Gaussian process flows

Pashupati Hegde, Markus Heinonen, Harri Lähdesmäki, Samuel Kaski

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

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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

Original language	English
Title of host publication	The 22nd International Conference on Artificial Intelligence and Statistic
Pages	1-15
Number of pages	16
Volume	89
Publication status	Published - Apr 2019
MoE publication type	A4 Article in a conference publication
Event	International Conference on Artificial Intelligence and Statistics - Naha, Japan Duration: 16 Apr 2019 → 18 Apr 2019 Conference number: 22

Publication series

Name	Proceedings of Machine Learning Research
Publisher	PMLR
Volume	89
ISSN (Electronic)	2640-3498

Conference

Conference	International Conference on Artificial Intelligence and Statistics
Abbreviated title	AISTATS
Country	Japan
City	Naha
Period	16/04/2019 → 18/04/2019

Keywords

gaussian process
Bayesian methods

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title = "Deep learning with differential Gaussian process flows",

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 = "gaussian process, Bayesian methods",

author = "Pashupati Hegde and Markus Heinonen and Harri L{\"a}hdesm{\"a}ki and Samuel Kaski",

year = "2019",

month = apr,

language = "English",

volume = "89",

series = "Proceedings of Machine Learning Research",

publisher = "PMLR",

pages = "1--15",

booktitle = "The 22nd International Conference on Artificial Intelligence and Statistic",

note = "International Conference on Artificial Intelligence and Statistics, AISTATS ; Conference date: 16-04-2019 Through 18-04-2019",

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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, pp. 1-15, International Conference on Artificial Intelligence and Statistics, Naha, Japan, 16/04/2019. <https://arxiv.org/abs/1810.04066>

Deep learning with differential Gaussian process flows. / Hegde, Pashupati ; Heinonen, Markus ; Lähdesmäki, Harri ; Kaski, Samuel.

The 22nd International Conference on Artificial Intelligence and Statistic. Vol. 89 2019. p. 1-15 (Proceedings of Machine Learning Research; Vol. 89).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

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AU - Hegde, Pashupati

AU - Heinonen, Markus

AU - Lähdesmäki, Harri

AU - Kaski, Samuel

N1 - Conference code: 22

PY - 2019/4

Y1 - 2019/4

N2 - 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

AB - 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

KW - gaussian process

KW - Bayesian methods

UR - https://arxiv.org/abs/1810.04066

M3 - Conference contribution

VL - 89

T3 - Proceedings of Machine Learning Research

SP - 1

EP - 15

BT - The 22nd International Conference on Artificial Intelligence and Statistic

T2 - International Conference on Artificial Intelligence and Statistics

Y2 - 16 April 2019 through 18 April 2019

ER -

Deep learning with differential Gaussian process flows

Abstract

Publication series

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Keywords

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