MIOLANE, Nina; GUIGUI, Nicolas; ZAATITI, Hadi; SHEWMAKE, Christian; HAJRI, Hatem; BROOKS, Daniel; LE BRIGANT, Alice; MATHE, Johan; HOU, Benjamin; THANWERDAS, Yann; HEYDER, Stefan; PELTRE, Olivier; KOEP, Niklas; CABANES, Yann; GERALD, Thomas; CHAUCHAT, Paul; KAINZ, Bernhard; DONNAT, Claire; HOLMES, Susan; PENNEC, Xavier

doi:10.25080/Majora-342d178e-007

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Licence d’utilisation du document

MIOLANE, Nina
Stanford University

GUIGUI, Nicolas
Université Côte d'Azur [UniCA]
Institut National de Recherche en Informatique et en Automatique [Inria]
E-Patient : Images, données & mOdèles pour la médeciNe numériquE [EPIONE]

ZAATITI, Hadi
IRT SystemX

Langue

Communication dans un congrès

Ce document a été publié dans

SciPy 2020 - 19th Python in Science Conference, 2020-07-06, Austin, Texas. p. 48-57

Résumé en anglais

There is a growing interest in leveraging differential geometry in the machine learning community. Yet, the adoption of the associated geometric computations has been inhibited by the lack of a reference implementation. Such an implementation should typically allow its users: (i) to get intuition on concepts from differential geometry through a hands-on approach, often not provided by traditional textbooks; and (ii) to run geometric machine learning algorithms seamlessly, without delving into the mathematical details. To address this gap, we present the open-source Python package geomstats and introduce hands-on tutorials for differential geometry and geometric machine learning algorithms-Geometric Learning-that rely on it. Code and documentation: github.com/geomstats/geomstats and geomstats.ai.< Réduire

Mots clés en anglais

Index Terms-differential geometry

statistics

manifold

machine learning

URI

https://oskar-bordeaux.fr/handle/20.500.12278/191912

DOI

http://dx.doi.org/10.25080/Majora-342d178e-007

Projet Européen

G-Statistics - Foundations of Geometric Statistics and Their Application in the Life Sciences

Project ANR

3IA Côte d'Azur

Origine

Importé de hal

Unités de recherche

Institut de Mathématiques de Bordeaux (IMB) - UMR 5251