Pytorch implementation of Hyperspherical Variational Auto-Encoders
Clustering routines for the unit sphere
Tensorflow implementation of Hyperspherical Variational Auto-Encoders
Code for EMNLP18 paper "Spherical Latent Spaces for Stable Variational Autoencoders"
Kernel density estimation on a sphere
This is the repository for the research project about the Generalized Procrustes Analysis using spatial anatomical information in fMRI data, i.e., the ProMises (Procrustes von Mises-Fisher) model
Sampling from the von Mises - Fisher distribution
Spherical statistics in Python
Fit and manipulate a few probability distribution functions on the unit S2 sphere.
Directional Co-clustering with a Conscience (DCC)
spherical clustering, von-Mises Fisher mixture model
Fast Generation of von Mises-Fisher Distributed Pseudo-Random Vectors
The following includes all the MATLAB scripts necessary for implementing the algorithm described in the attached paper.
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