Machine Learning, Math

The geometry of data: the missing metric tensor and the Stein score [Part II]

Credit: ESA/Webb, NASA & CSA, J. Rigby. / The James Webb Space Telescope captures gravitational lensing, a phenomenon that can be modeled using differential geometry.

Note: This is a continuation of the previous post: Thoughts on Riemannian metrics and its connection with diffusion/score matching [Part I], so if you haven’t read it yet, please consider reading as I won’t be re-introducing in depth the concepts (e.g., the two scores) that I described there already. This article became a bit long, so if you are familiar already with metric tensors and differential geometry you can just skip the first part.

I was planning to write a paper about this topic, but my spare time is not that great so I decided it would be much more fun and educative to write this article in form of a tutorial. If you liked it, please consider citing it:

Cite this article as: Christian S. Perone, "The geometry of data: the missing metric tensor and the Stein score [Part II]," in Terra Incognita, 12/11/2024, https://blog.christianperone.com/2024/11/the-geometry-of-data-part-ii/.

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