https://github.com/emaballarin/fastwonn

🏁🥈Fast, GPU-friendly, differentiable computation of Intrinsic Dimension via Maximum Likelihood, the TwoNN algorithm, and everything in between!
https://github.com/emaballarin/fastwonn

Last synced: about 1 month ago
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🏁🥈Fast, GPU-friendly, differentiable computation of Intrinsic Dimension via Maximum Likelihood, the TwoNN algorithm, and everything in between!

• Host: GitHub
• URL: https://github.com/emaballarin/fastwonn
• Owner: emaballarin
• Created: 2024-06-30T17:02:14.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-07-08T01:37:26.000Z (6 months ago)
• Last Synced: 2024-08-09T18:37:20.749Z (5 months ago)
• Language: Python
• Homepage: https://pypi.org/project/fastwonn/
• Size: 26.4 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# `fastwonn`

Fast, GPU-friendly, differentiable computation of Intrinsic Dimension via Maximum Likelihood (Levina & Bickel, 2004), the TwoNN algorithm (Facco et al., 2017), and everything in between!

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### References

- [E. Levina, P. Bickel; "Maximum Likelihood Estimation of Intrinsic Dimension", Advances in Neural Information Processing Systems, 2004](https://papers.nips.cc/paper_files/paper/2004/hash/74934548253bcab8490ebd74afed7031-Abstract.html)

- [E. Facco, M. d'Errico, A. Rodriguez, A. Laio; "Estimating the intrinsic dimension of datasets by a minimal neighborhood information", Nature Scientific Reports, 2017](https://www.nature.com/articles/s41598-017-11873-y)