Geomstats: A Python Package for Riemannian Geometry in Machine Learning |SciPy 2020| Miolane 

We introduce `geomstats`, an open-source Python package for computations and statistics for data on non-linear manifolds such as hyperbolic spaces, spaces of symmetric positive definite matrices, Lie groups of transformations, etc. We provide object-oriented and extensively unit-tested implementations. The manifolds come with families of Riemannian metrics, with associated Exponential/Logarithm maps, geodesics, and parallel transport. The learning algorithms follow scikit-learn API and provide methods for estimation, clustering and dimension reduction on manifolds. The operations are vectorized for batch computations and available with NumPy, PyTorch, and TensorFlow backends, which allows GPU acceleration. This talk will present the package, compare it with related libraries, and show relevant examples. Code and documentation: www.geomstats.ai.
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