Riemannian Adaptive Optimization Methods with pytorch optim
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Updated
Apr 28, 2024 - Python
Riemannian Adaptive Optimization Methods with pytorch optim
A C++ library of Markov Chain Monte Carlo (MCMC) methods
Riemannian stochastic optimization algorithms: Version 1.0.3
Regression Graph Neural Network (regGNN) for cognitive score prediction.
Implementation of Deep SPDNet in pytorch
Sensitivity Analysis of Deep Neural Networks (AAAI-19 paper)
Subsampled Riemannian trust-region (RTR) algorithms
Measure the distance between two spectra/signals using optimal transport and related metrics
Notes prepared for seminars, compiled from books, or taken in classes are included in this repository. There might be some notes prepared by other seminar participants, which are labelled accordingly.
Riemannian metrics to measure distances in latent space of VAEs
C++ library for meshes and finite elements on manifolds
Matlab implementation of paper "Principal Geodesic Analysis in the Space of Discrete Shells", SGP-2018
Code implementations of the methods discussed in Generalized Fiducial Inference on Differentiable Manifolds by A. Murph, J. Hannig, and J. Williams.
The code for vector transport free LBFGS quasi-Newton's optimization on the Riemannian manifolds
Implementing the algorithms of Kim et al. 2014 for regressing multiple symmetric positive definite matrices against real valued covariates.
Optimised Orientation Tracking using Riemann Stochastic Gradient Descent (RSGD)
Project in Advanced Robotics course project at SDU 21/22. Implementation of learning method for skills for arm robots based on GMM with Rieamannian Manifolds
Algorithms for the approximation of an embedding for Markov chains.
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