Comment by 1024core
2 months ago
> Why not, for example, a wavelet transform.
That is a great idea for a paper. Work on it, write it up and please be sure to put my name down as a co-author ;-)
2 months ago
> Why not, for example, a wavelet transform.
That is a great idea for a paper. Work on it, write it up and please be sure to put my name down as a co-author ;-)
Or for that matter, a transform that's learned from the data :) A neural net for the transform itself!
That would be super cool if it works! I’ve also wondered the same thing about activation functions. Why not let the algorithm learn the activation function?
This idea exists (the broad field is called neural architecture search), although you have to parameterize it somehow to allow gradient descent to happen.
Here are examples:
https://arxiv.org/abs/2009.04759
https://arxiv.org/abs/1906.09529
Mostly because of computational efficiency irrc, the non linearity doesn’t seem to have much impact, so picking one that’s fast is a more efficient use of limited computational resources.