Geomstats: Geometry in Machine Learning

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Computations and statistics on manifolds with geometric structures: Github

• To get started with geomstats, see the examples and notebook directories
• For more in-depth applications of geomstats, see the application repository
• The documentation of geomstats can be found on the documentation website
• If you want to stay up-to-date with geometric statistics and learning, follow our twitter-bot.
• If you find geomstats useful, please kindly cite our paper:
  • Miolane, N., et al. Geomstats: A Python Package for Riemannian Geometry in Machine Learning. (2020)
• Contact us if you are interested in contributing!

Challenges

• ICLR Computational Geometry & Topology Challenge 2022
  • The purpose of this challenge was to foster reproducible research in geometric (deep) learning, by crowdsourcing the open-source implementation of learning algorithms on manifolds.
  • Read more here:
    • Myers, A., Utpala, S., Talbar, S., Sanborn, S., Shewmake, C., Donnat, C., including Miolane, N. ICLR 2022 Challenge for Computational Geometry and Topology: Design and Results
• ICLR 2021 Challenge for Computational Geometry & Topology

  • The competition asked participants to provide creative contributions to the fields of computational geometry and topology through the open-source repositories Geomstats and Giotto-TDA.

  • Read more here: 

    • Miolane, N., et al. ICLR 2021 Challenge for Computational Geometry & Topology: Design and Results​