fastwonn

Name	fastwonn JSON
Version	0.0.9 JSON
	download
home_page	https://github.com/emaballarin/fastwonn
Summary	Fast, GPU-friendly, differentiable computation of Intrinsic Dimension via Maximum Likelihood, the TwoNN algorithm, and everything in between!
upload_time	2024-07-08 01:38:03
maintainer	None
docs_url	None
author	Emanuele Ballarin
requires_python	>=3.10
license	MIT
keywords	deep learning differentiable programming intrinsic dimension machine learning manifold learning maximum likelihood estimation pytorch twonn
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            # `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!

---

### 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)

            

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