[medium_spicy]

Short answer: if the inputs can be represented well on the Fourier basis, yes. I have a patent in process on this, fingers crossed.

Longer answer: deep learning models are usually trying to find the best nonlinear basis in which to represent inputs; if the inputs are well-represented (read that as: can be sparsely represented) in some basis known a-priori, it usually helps to just put them in that basis, e.g., by FFT’ing RF signals.

The challenge is that the overall-optimal basis might not be the same as those of any local minima, so you’ve got to do some tricks to nudge the network closer.