[yobbo]

https://kindxiaoming.github.io/pykan/intro.html

At the end of this example, they recover the symbolic formula that generated their training set: exp(x₂² + sin(3.14x₁)).

It's like a computation graph with a library of "activation functions" that is optimised, and then pruned. You can recover good symbolic formulas from the pruned graph.

Maybe not meaningful for MNIST.

[beagle3]

I wonder if Breiman’s ACE (alternating conditional expectation) is useful as a building block here.

It will easily recover this formula, because it is separable under the log transformation (which ACE recovers as well).

But ACE doesn’t work well on unseparable problems - not sure how well KAN will.