| hal.structure.identifier | Department of Statistics [Stanford] | |
| dc.contributor.author | MIOLANE, Nina | |
| hal.structure.identifier | Université Côte d'Azur [UniCA] | |
| hal.structure.identifier | E-Patient : Images, données & mOdèles pour la médeciNe numériquE [EPIONE] | |
| dc.contributor.author | GUIGUI, Nicolas | |
| hal.structure.identifier | Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) [SAMM] | |
| dc.contributor.author | LE BRIGANT, Alice | |
| hal.structure.identifier | Frog labs AI San Francisco | |
| dc.contributor.author | MATHE, Johan | |
| hal.structure.identifier | Imperial College London | |
| dc.contributor.author | HOU, Benjamin | |
| dc.contributor.author | THANWERDAS, Yann | |
| hal.structure.identifier | Technische Universität Ilmenau [TU ] | |
| dc.contributor.author | HEYDER, Stefan | |
| hal.structure.identifier | Institut de Mathématiques de Jussieu - Paris Rive Gauche [IMJ-PRG (UMR_7586)] | |
| dc.contributor.author | PELTRE, Olivier | |
| hal.structure.identifier | Rheinisch-Westfälische Technische Hochschule Aachen University [RWTH] | |
| dc.contributor.author | KOEP, Niklas | |
| hal.structure.identifier | IRT SystemX | |
| dc.contributor.author | ZAATITI, Hadi | |
| hal.structure.identifier | IRT SystemX | |
| dc.contributor.author | HAJRI, Hatem | |
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| dc.contributor.author | CABANES, Yann | |
| hal.structure.identifier | Machine Learning and Information Access [MLIA] | |
| dc.contributor.author | GERALD, Thomas | |
| hal.structure.identifier | Centre de Robotique [CAOR] | |
| dc.contributor.author | CHAUCHAT, Paul | |
| hal.structure.identifier | Washington University in Saint Louis [WUSTL] | |
| dc.contributor.author | SHEWMAKE, Christian | |
| hal.structure.identifier | Chercheur indépendant | |
| dc.contributor.author | BROOKS, Daniel | |
| hal.structure.identifier | Imperial College London | |
| dc.contributor.author | KAINZ, Bernhard | |
| hal.structure.identifier | Stanford University | |
| dc.contributor.author | DONNAT, Claire | |
| hal.structure.identifier | Department of Statistics [Stanford] | |
| dc.contributor.author | HOLMES, Susan | |
| hal.structure.identifier | Université Côte d'Azur [UniCA] | |
| hal.structure.identifier | E-Patient : Images, données & mOdèles pour la médeciNe numériquE [EPIONE] | |
| dc.contributor.author | PENNEC, Xavier | |
| dc.date.accessioned | 2024-04-04T02:47:57Z | |
| dc.date.available | 2024-04-04T02:47:57Z | |
| dc.date.created | 2020-07 | |
| dc.date.issued | 2020-12-20 | |
| dc.identifier.issn | 1532-4435 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/191694 | |
| dc.description.abstractEn | We introduce Geomstats, an open-source Python toolbox for computations and statistics on nonlinear manifolds, such as hyperbolic spaces, spaces of symmetric positive definite matrices, Lie groups of transformations, and many more. We provide object-oriented and extensively unit-tested implementations. Among others, manifolds come equipped with families of Riemannian metrics, with associated exponential and logarithmic maps, geodesics and parallel transport. Statistics and learning algorithms provide methods for estimation, clustering and dimension reduction on manifolds. All associated operations are vectorized for batch computation and provide support for different execution backends, namely NumPy, PyTorch and TensorFlow, enabling GPU acceleration. This paper presents the package, compares it with related libraries and provides relevant code examples. We show that Geomstats provides reliable building blocks to foster research in differential geometry and statistics, and to democratize the use of Riemannian geometry in machine learning applications. The source code is freely available under the MIT license at http://geomstats.ai. | |
| dc.description.sponsorship | Idex UCA JEDI - ANR-15-IDEX-0001 | |
| dc.description.sponsorship | 3IA Côte d'Azur - ANR-19-P3IA-0002 | |
| dc.language.iso | en | |
| dc.publisher | Microtome Publishing | |
| dc.rights.uri | http://creativecommons.org/licenses/by/ | |
| dc.subject.en | differential geometry | |
| dc.subject.en | Riemannian geometry | |
| dc.subject.en | statistics | |
| dc.subject.en | machine learning | |
| dc.subject.en | manifold | |
| dc.title.en | Geomstats: A Python Package for Riemannian Geometry in Machine Learning | |
| dc.type | Article de revue | |
| dc.subject.hal | Informatique [cs] | |
| dc.subject.hal | Mathématiques [math]/Géométrie différentielle [math.DG] | |
| dc.description.sponsorshipEurope | G-Statistics - Foundations of Geometric Statistics and Their Application in the Life Sciences | |
| bordeaux.journal | Journal of Machine Learning Research | |
| bordeaux.page | 1-9 | |
| bordeaux.volume | 21 | |
| bordeaux.hal.laboratories | Institut de Mathématiques de Bordeaux (IMB) - UMR 5251 | * |
| bordeaux.issue | 223 | |
| bordeaux.institution | Université de Bordeaux | |
| bordeaux.institution | Bordeaux INP | |
| bordeaux.institution | CNRS | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | hal-02536154 | |
| hal.version | 1 | |
| hal.popular | non | |
| hal.audience | Internationale | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-02536154v1 | |
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