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Introduction to Geometric Learning in Python with Geomstats

hal.structure.identifierStanford University
dc.contributor.authorMIOLANE, Nina
hal.structure.identifierUniversité Côte d'Azur [UniCA]
hal.structure.identifierInstitut National de Recherche en Informatique et en Automatique [Inria]
hal.structure.identifierE-Patient : Images, données & mOdèles pour la médeciNe numériquE [EPIONE]
dc.contributor.authorGUIGUI, Nicolas
hal.structure.identifierIRT SystemX
dc.contributor.authorZAATITI, Hadi
hal.structure.identifierWashington University in Saint Louis [WUSTL]
dc.contributor.authorSHEWMAKE, Christian
hal.structure.identifierIRT SystemX
dc.contributor.authorHAJRI, Hatem
hal.structure.identifierThales Air Systems
hal.structure.identifierMachine Learning and Information Access [MLIA]
dc.contributor.authorBROOKS, Daniel
hal.structure.identifierStatistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) [SAMM]
dc.contributor.authorLE BRIGANT, Alice
hal.structure.identifierFrog labs AI San Francisco
dc.contributor.authorMATHE, Johan
hal.structure.identifierImperial College London
dc.contributor.authorHOU, Benjamin
dc.contributor.authorTHANWERDAS, Yann
hal.structure.identifierTechnische Universität Ilmenau [TU ]
dc.contributor.authorHEYDER, Stefan
hal.structure.identifierInstitut de Mathématiques de Jussieu - Paris Rive Gauche [IMJ-PRG (UMR_7586)]
dc.contributor.authorPELTRE, Olivier
hal.structure.identifierRWTH Aachen University = Rheinisch-Westfälische Technische Hochschule Aachen [RWTH Aachen]
dc.contributor.authorKOEP, Niklas
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorCABANES, Yann
hal.structure.identifierMachine Learning and Information Access [MLIA]
dc.contributor.authorGERALD, Thomas
hal.structure.identifierCentre de Robotique [CAOR]
dc.contributor.authorCHAUCHAT, Paul
hal.structure.identifierImperial College London
dc.contributor.authorKAINZ, Bernhard
hal.structure.identifierStanford University
dc.contributor.authorDONNAT, Claire
hal.structure.identifierStanford University
dc.contributor.authorHOLMES, Susan
hal.structure.identifierUniversité Côte d'Azur [UniCA]
hal.structure.identifierE-Patient : Images, données & mOdèles pour la médeciNe numériquE [EPIONE]
dc.contributor.authorPENNEC, Xavier
dc.contributor.editorMeghann Agarwal
dc.contributor.editorChris Calloway
dc.contributor.editorDillon Niederhut
dc.contributor.editorDavid Shupe
dc.date.accessioned2024-04-04T02:50:27Z
dc.date.available2024-04-04T02:50:27Z
dc.date.conference2020-07-06
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/191912
dc.description.abstractEnThere 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.
dc.description.sponsorship3IA Côte d'Azur - ANR-19-P3IA-0002
dc.language.isoen
dc.subject.enIndex Terms-differential geometry
dc.subject.enstatistics
dc.subject.enmanifold
dc.subject.enmachine learning
dc.title.enIntroduction to Geometric Learning in Python with Geomstats
dc.typeCommunication dans un congrès
dc.identifier.doi10.25080/Majora-342d178e-007
dc.subject.halMathématiques [math]/Géométrie différentielle [math.DG]
dc.subject.halMathématiques [math]/Statistiques [math.ST]
dc.description.sponsorshipEuropeG-Statistics - Foundations of Geometric Statistics and Their Application in the Life Sciences
bordeaux.page48-57
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.conference.titleSciPy 2020 - 19th Python in Science Conference
bordeaux.countryUS
bordeaux.conference.cityAustin, Texas
bordeaux.peerReviewedoui
hal.identifierhal-02908006
hal.version1
hal.invitednon
hal.proceedingsoui
hal.conference.end2020-07-12
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02908006v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.spage=48-57&rft.epage=48-57&rft.au=MIOLANE,%20Nina&GUIGUI,%20Nicolas&ZAATITI,%20Hadi&SHEWMAKE,%20Christian&HAJRI,%20Hatem&rft.genre=unknown


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