There have been some interesting advances in trying to add spectral information to the data that a learning architecture has at its disposal, but there are a couple roadblocks that I don’t think have been solved yet.

1. Complex-valued NNs are not an easy generalization of real ones.

2. A localization in one domain implies non-local behavior in the other (this is the Fourier uncertainty principle).

Fourier Neural Operators (FNOs) come close to what I want to see in this area but since they enforce sparsity in the spectral domain their application is necessarily limited.

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FuckButtons 2 hours ago | parent [-]

I do wonder if a wavelet transform might be better.

	▲	TimorousBestie an hour ago \| parent [-]
		I think one can do better with a wavelet, shearlet, or curvelet transform that is adapted to the problem domain at hand. But the uncertainty principle still haunts those transforms, and anyway the goal is to be domain-agile.