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