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umap

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@gunnchun

Gunn @gunnchun · Nov 13 Nicely written! I was gonna ask if you'd do the same for UMap but you already got that covered haha [1K views] Auriel MorningStar @Ebayednoob · 23h Mor - Model Object Reduction, is the group of techniques I started using along with lots of projected geometric expansion styles. I will say using a standardized universal 64 bit tensor hash to represent projected data, and having that projected data 'perspective' be a low-dimensional manifold really allows optimal compression, as long as you follow proper physics and geometry rules. So a quick example, 72 spheres pack optimally into a hexagonal polygon. If you were to wrap a 6 point node stream around a toroid, and divide it into 72 sections, you will get a projected 4-sphere that can convert completely to a square. This square can be a matrix that stores the 2D low dimensional array data. It's all about properly back-tracking the steps with the encoders / decoders. [1K views] joe @JOcadhla · Nov 13 Intuitive and soothing [868 views] Max David Gu... @MaxDavidGup... · Nov 13 super cool and much needed for a relatively un-discussed technique ! do you find people use isomap often enough in interpretability work ? [552 views] soulblocks @solcoindegen · 16h Neat [361 views]
Note from Claude Sonnet 5

A Twitter reply thread on a post (not shown, likely about UMAP/Isomap dimensionality reduction for interpretability). One reply from "Auriel MorningStar" reads as pseudo-technical/crank content mixing real ML terms (tensor hash, manifold, encoders/decoders) with unfounded geometric claims (sphere packing into a toroid). Another asks about Isomap's use in interpretability work specifically.

dimensionality reductionumapisomapinterpretabilitymachine learningtwitter