Introduction to Geometric Learning in Python with Geomstats - Inria - Institut national de recherche en sciences et technologies du numérique
Conference Papers Year : 2020

Introduction to Geometric Learning in Python with Geomstats

Nina Miolane
  • Function : Author
  • PersonId : 951696
  • IdRef : 199540519
Hadi Zaatiti
Christian Shewmake
Hatem Hajri
  • Function : Author
  • PersonId : 888500
  • IdRef : 156227835
Benjamin Hou
  • Function : Author
  • PersonId : 1086602
Yann Thanwerdas
Stefan Heyder
Yann Cabanes
Thomas Gerald
Paul Chauchat
Bernhard Kainz
Claire Donnat
Susan Holmes
  • Function : Author
  • PersonId : 943876

Abstract

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.
Fichier principal
Vignette du fichier
geomstats.pdf (2.77 Mo) Télécharger le fichier
Origin Files produced by the author(s)
Loading...

Dates and versions

hal-02908006 , version 1 (28-07-2020)

Identifiers

Cite

Nina Miolane, Nicolas Guigui, Hadi Zaatiti, Christian Shewmake, Hatem Hajri, et al.. Introduction to Geometric Learning in Python with Geomstats. SciPy 2020 - 19th Python in Science Conference, Jul 2020, Austin, Texas, United States. pp.48-57, ⟨10.25080/Majora-342d178e-007⟩. ⟨hal-02908006⟩
1078 View
3347 Download

Altmetric

Share

More