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> 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 ;-

• monkfish328 2y ago
  Or for that matter, a transform that's learned from the data :) A neural net for the transform itself!
  • spot5010 2y ago
    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?
    • porridgeraisin 2y ago
      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/2009.04759 https://arxiv.org/abs/1906.09529 https://arxiv.org/abs/1906.09529
    • FuckButtons 2y ago
      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.