Visualiser for basic geometric primitives and fractals in arbitrary-dimensional spaces
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Updated
Mar 12, 2017 - C++
Visualiser for basic geometric primitives and fractals in arbitrary-dimensional spaces
Implementation for <Orthogonal Over-Parameterized Training> in CVPR'21.
Code accompanying the paper "360 Surface Regression with a Hyper-Sphere Loss", 3DV 2019
Implementation for <Learning with Hyperspherical Uniformity> in AISTATS'21.
The Similarity Search Tree is an efficient method for indexing high dimensional feature vectors. The main objective of this data structure is to obtain the nearest neighbors given a certain query vector in a reasonable amount of time. In this project, the k-NN algorithm was adapted for supporting image retrieval.
montecarlo methods
Introduces a framework for making non-canonical hyperspherical coordinate systems.
Hypersphere volume computation for low dimensionality using Monte Carlo techniques and threaded Ruby
Count and/or enumerate the lattice points that lie within a hypersphere
This document purpose is to demonstrate hyperspace volume in n dimensions.
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