MachineLearningLifeScience/stochman
Algorithms for computations on random manifolds made easier
This project helps machine learning researchers and scientists working with complex, noisy data understand the underlying geometric structure. It takes in neural network models or descriptions of data geometry and outputs tools for calculating how data points relate to each other in this complex space, helping to analyze and interpret challenging datasets. Researchers in areas like computational biology or physics, who deal with high-dimensional, non-Euclidean data, would find this valuable.
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Use this if you are a machine learning researcher or scientist working with data that lies on a 'manifold' (a curved space) and need to compute distances or paths between data points within that space.
Not ideal if you are primarily interested in standard Euclidean data analysis or don't need to model the underlying geometry of your data.
Stars
94
Forks
10
Language
Python
License
Apache-2.0
Category
Last pushed
Dec 04, 2023
Commits (30d)
0
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