Master project: Bures-Wasserstein barycenters

Distortion-corrected kernel density estimate on Riemannian manifolds

Essential Books for Computer Science

GitHub repository for ”Learning a Discriminative Grassmannian Neural Network for Visual Classification“

A package providing tractable examples of parallel transport for several matrix manifolds

Geometrical Layers for Pytorch Neural Networks

Riemannian Adaptive Optimization Methods with pytorch optim

A C++ library of Markov Chain Monte Carlo (MCMC) methods

Algorithms for the approximation of an embedding for Markov chains.

Implementation of Deep SPDNet in pytorch

Code implementations of the methods discussed in Generalized Fiducial Inference on Differentiable Manifolds by A. Murph, J. Hannig, and J. Williams.

MATH-512 Optimization on Manifolds Spring 2023 Project 1: Gaussian Mixture Models

Regression Graph Neural Network (regGNN) for cognitive score prediction.

Dimensionality reduction on manifold of SPD matrices, based on pymanopt implementation

Optimised Orientation Tracking using Riemann Stochastic Gradient Descent (RSGD)

Sensitivity Analysis of Deep Neural Networks (AAAI-19 paper)

Riemannian stochastic optimization algorithms: Version 1.0.3

Subsampled Riemannian trust-region (RTR) algorithms

Measure the distance between two spectra/signals using optimal transport and related metrics

リーマン多様体上の最適化