@inproceedings{miolane:hal-02908006,
  TITLE = {{Introduction to Geometric Learning in Python with Geomstats}},
  AUTHOR = {Miolane, Nina and Guigui, Nicolas and Zaatiti, Hadi and Shewmake, Christian and Hajri, Hatem and Brooks, Daniel and Le Brigant, Alice and Mathe, Johan and Hou, Benjamin and Thanwerdas, Yann and Heyder, Stefan and Peltre, Olivier and Koep, Niklas and Cabanes, Yann and Gerald, Thomas and Chauchat, Paul and Kainz, Bernhard and Donnat, Claire and Holmes, Susan and Pennec, Xavier},
  URL = {https://hal.inria.fr/hal-02908006},
  BOOKTITLE = {{SciPy 2020 - 19th Python in Science Conference}},
  ADDRESS = {Austin, Texas, United States},
  EDITOR = {Meghann Agarwal and Chris Calloway and Dillon Niederhut and David Shupe},
  PAGES = {48-57},
  YEAR = {2020},
  MONTH = Jul,
  DOI = {10.25080/Majora-342d178e-007},
  KEYWORDS = {Index Terms-differential geometry ; statistics ; manifold ; machine learning},
  PDF = {https://hal.inria.fr/hal-02908006/file/geomstats.pdf},
  HAL_ID = {hal-02908006},
  HAL_VERSION = {v1},
  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/geomstatsand geomstats.ai.},
  code = {https://github.com/geomstats/geomstats},
}