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Geomstats: A Python Package for Riemannian Geometry in Mach..:

Miolane, Nina ; Guigui, Nicolas ; Le Brigant, Alice...
info:eu-repo/grantAgreement//786854/EU/G-Statistics - Foundations of Geometric Statistics and Their Application in the Life Sciences/ERC AdG. , 2020

Link: https://inria.hal.science/hal-02536154

RT Journal
T1 <Title>Geomstats: A Python Package for Riemannian Geometry in Machine Learning</Title>

UL https://suche.suub.uni-bremen.de/peid=base-ftanrparis:oai:HAL:hal-02536154v2&Exemplar=1&LAN=DE
				
A1 Miolane, Nina
A1 Guigui, Nicolas
A1 Le Brigant, Alice
A1 Mathe, Johan
A1 Hou, Benjamin
A1 Thanwerdas, Yann
A1 Heyder, Stefan
A1 Peltre, Olivier
A1 Koep, Niklas
A1 Zaatiti, Hadi
A1 Hajri, Hatem
A1 Cabanes, Yann
A1 Gerald, Thomas
A1 Chauchat, Paul
A1 Shewmake, Christian
A1 Brooks, Daniel
A1 Kainz, Bernhard
A1 Donnat, Claire
A1 Holmes, Susan
A1 Pennec, Xavier
PB HAL CCSD; Microtome Publishing
YR 2020
K1 differential geometry
K1 Riemannian geometry
K1 statistics
K1 machine learning
K1 manifold
K1 [INFO]Computer Science [cs]
K1 [MATH.MATH-DG]Mathematics [math]/Differential Geometry [math.DG]
JF info:eu-repo/grantAgreement//786854/EU/G-Statistics - Foundations of Geometric Statistics and Their Application in the Life Sciences/ERC AdG
LK http://dx.doi.org/https://inria.hal.science/hal-02536154
DO https://inria.hal.science/hal-02536154
SF ELIB - SuUB Bremen

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