Introduction to Geometric Learning in Python with Geomstats
| العنوان: | Introduction to Geometric Learning in Python with Geomstats |
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| المؤلفون: | Nina Miolane, Paul Chauchat, Xavier Pennec, Bernhard Kainz, Daniel Brooks, Susan Holmes, Johan Mathe, Niklas Koep, Stefan Heyder, Benjamin Hou, Alice Le Brigant, Christian Shewmake, Yann Thanwerdas, Yann Cabanes, Thomas Gerald, Olivier Peltre, Nicolas Guigui, Claire Donnat, Hadi Zaatiti, Hatem Hajri |
| المساهمون: | Stanford University, Université Côte d'Azur (UCA), Institut National de Recherche en Informatique et en Automatique (Inria), E-Patient : Images, données & mOdèles pour la médeciNe numériquE (EPIONE), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), IRT SystemX, Washington University in Saint Louis (WUSTL), Thales Air Systems, THALES [France], Machine Learning and Information Access (MLIA), LIP6, Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne) (SAMM), Université Paris 1 Panthéon-Sorbonne (UP1), Frog labs AI San Francisco, Imperial College London, Technische Universität Ilmenau (TU ), Institut de Mathématiques de Jussieu - Paris Rive Gauche (IMJ-PRG (UMR_7586)), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Rheinisch-Westfälische Technische Hochschule Aachen University (RWTH), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Centre de Robotique (CAOR), Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Meghann Agarwal, Chris Calloway, Dillon Niederhut, David Shupe, ANR-19-P3IA-0002,3IA@cote d'azur,3IA Côte d'Azur(2019), European Project: 786854,H2020 Pilier ERC,ERC AdG(2018), IRT SystemX (IRT SystemX), Thales Group [France], Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Rheinisch-Westfälische Technische Hochschule Aachen (RWTH), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), MINES ParisTech - École nationale supérieure des mines de Paris |
| المصدر: | SciPy 2020-19th Python in Science Conference SciPy 2020-19th Python in Science Conference, Jul 2020, Austin, Texas, United States. pp.48-57, ⟨10.25080/Majora-342d178e-007⟩ Proceedings of the 19th Python in Science Conference |
| بيانات النشر: | HAL CCSD, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | manifold, Programming language, Computer science, Computation, Python (programming language), computer.software_genre, Index Terms-differential geometry, Documentation, machine learning, Differential geometry, statistics, [MATH.MATH-DG]Mathematics [math]/Differential Geometry [math.DG], [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Reference implementation, computer, Intuition, computer.programming_language |
| الوصف: | International audience; 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. |
| اللغة: | English |
| تدمد: | 2575-9752 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f3547d9c37a81d93725731a1eb934095 https://inria.hal.science/hal-02908006/file/geomstats.pdf |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....f3547d9c37a81d93725731a1eb934095 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 25759752 |
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