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

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

15 Sitaatiot (Scopus)

114 Lataukset (Pure)

Abstrakti

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

Alkuperäiskieli	Englanti
Otsikko	The 22nd International Conference on Artificial Intelligence and Statistic
Kustantaja	JMLR
Sivut	1-15
Sivumäärä	16
Vuosikerta	89
Tila	Julkaistu - huhtik. 2019
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	International Conference on Artificial Intelligence and Statistics - Naha, Japani Kesto: 16 huhtik. 2019 → 18 huhtik. 2019 Konferenssinumero: 22

Julkaisusarja

Nimi	Proceedings of Machine Learning Research
Kustantaja	PMLR
Vuosikerta	89
ISSN (elektroninen)	2640-3498

Conference

Conference	International Conference on Artificial Intelligence and Statistics
Lyhennettä	AISTATS
Maa/Alue	Japani
Kaupunki	Naha
Ajanjakso	16/04/2019 → 18/04/2019

Pääsy asiakirjaan

hegde19aAccepted author manuscript, 8,83 MB

https://proceedings.mlr.press/v89/hegde19a.html

Interaktiivinen koneoppiminen useista biodatalähteistä
Kaski, S. (Vastuullinen tutkija), Hämäläinen, A. (Projektin jäsen), Gadd, C. (Projektin jäsen), Hegde, P. (Projektin jäsen), Shen, Z. (Projektin jäsen), Siren, J. (Projektin jäsen), Trinh, T. (Projektin jäsen), Jain, A. (Projektin jäsen) & Jälkö, J. (Projektin jäsen)
01/01/2019 → 31/08/2021
Projekti: Academy of Finland: Other research funding
Helppotulkintainen tekoäly
Kaski, S. (Vastuullinen tutkija), Peltola, T. (Projektin jäsen), Daee, P. (Projektin jäsen) & Çelikok, M. M. (Projektin jäsen)
01/01/2018 → 31/12/2019
Projekti: Academy of Finland: Other research funding
Seuraavan sukupolven tilastollinen oppiminen synteettisten entsyymien muokkaukseen
Heinonen, M. (Vastuullinen tutkija)
01/09/2016 → 31/08/2019
Projekti: Academy of Finland: Other research funding

Science-IT
Hakala, M. (Manager)
Perustieteiden korkeakoulu
Laitteistot/tilat: Facility

Siteeraa tätä

@inproceedings{800994a5a0814cacbf6a2ca49a218cef,

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 = "JMLR",

pages = "1--15",

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

address = "United States",

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

}

Hegde, P, Heinonen, M , Lähdesmäki, H & Kaski, S 2019, Deep learning with differential Gaussian process flows. julkaisussa The 22nd International Conference on Artificial Intelligence and Statistic. Vuosikerta 89, Proceedings of Machine Learning Research, Vuosikerta 89, JMLR, Sivut 1-15, International Conference on Artificial Intelligence and Statistics, Naha, Japani, 16/04/2019. <https://proceedings.mlr.press/v89/hegde19a.html>

Deep learning with differential Gaussian process flows. / Hegde, Pashupati; Heinonen, Markus ; Lähdesmäki, Harri et al.
The 22nd International Conference on Artificial Intelligence and Statistic. Vuosikerta 89 JMLR, 2019. s. 1-15 (Proceedings of Machine Learning Research; Vuosikerta 89).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

TY - GEN

T1 - Deep learning with differential Gaussian process flows

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

M3 - Conference article in proceedings

VL - 89

T3 - Proceedings of Machine Learning Research

SP - 1

EP - 15

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

PB - JMLR

T2 - International Conference on Artificial Intelligence and Statistics

Y2 - 16 April 2019 through 18 April 2019

ER -

Deep learning with differential Gaussian process flows

Abstrakti

Julkaisusarja

Conference

Pääsy asiakirjaan

Sormenjälki

Projektit

Interaktiivinen koneoppiminen useista biodatalähteistä

Helppotulkintainen tekoäly

Seuraavan sukupolven tilastollinen oppiminen synteettisten entsyymien muokkaukseen

Laitteet

Science-IT

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