Interesting, from my perspective, they gave you everything you needed to reimplement this method with that description of the W_M x W_M matrix![0] You know wha

Interesting, from my perspective, they gave you everything you needed to reimplement this method with that description of the W_M x W_M matrix![0] You know what related idea from programming that corresponds to? Dynamic dispatch and type overloading. Let’s say you had a datetime type that measured time in eg Wednesdays since July 1st 1983. But then you wanted to re-parameterize that in terms of unix time eg seconds since Dec 31 1969. So you add a routine to convert the old format to the new format such that it represents the same thing. Turns out many ml primitives have associated parameters and parameterizations and can be converted into equivalent things cast in a new parameterization. I think the real contribution here is probably the completeness of the library of implemented a->b transformations, and the method for optimizing the overall distortion when things have to be lossily approximated. For related (but not really overlapping) works you might want to look into Git Re-basin and ZipIt! Merge, as well as the numerous quantization methods. [0] that said, much like almost all ml projects, the juicy bit will probably have a very concise description, but in reality require thousands of lines of code to make concrete..