GEOMSTATS -a Python Package for Riemannian Geometry in Machine Learning
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last edited by Geo-Sci-InfoPython package that performs computations on manifolds such as hyperspheres, hyperbolic spaces, spaces of symmetric positive definite matrices and Lie groups of transformations.
WEBSITE PYTHON PROJECT
GITHUB DEPOSITORYArxiv paper april 2020 - Geomstats: A Python Package for Riemannian Geometry in Machine Learning
References
- Nina Miolane, Johan Mathe, Claire Donnat, Mikael Jorda, Xavier Pennec, geomstats: a Python Package for Riemannian Geometry in Machine Learning 2018 PDF
- Benjamin Hou , Nina Miolane, Bishesh Khanal, Matthew C.H. Lee , Amir Alansary,
Steven McDonagh, Joseph Hajnal, Daniel Rueckert, Ben Glocker, Bernhard Kainz, Deep Pose Estimation for Image-Based Registration PDF
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last edited byInterested in contributing to geomstats? We welcome and recognize all contributions from documentation to testing to code development. Here are examples of ways you can contribute:
- Look up one of the starter projects: https://github.com/geomstats/geomstats/issues
- Submit a pull request with an example of code using geomstats: https://github.com/geomstats/examples
- Submit a pull request in the main library: https://github.com/geomstats/geomstats. We ask that each contribution in this repo comes with corresponding unit tests.
If you are new to the project, don't forget to add your name there!
