3 ms·
https://kindxiaoming.github.io/pykan/intro.html https://kindxiaoming.github.io/pykan/intro.html At the end of this example, they recover the symbolic formula t
by yobbo 2y ago
https://kindxiaoming.github.io/pykan/intro.html 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 2y agoI 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.