FFT's inputs are discrete to begin with (evenly spaced samples in the time domain), so I kind of see what you're trying to say... but I don't fully understand t

FFT's inputs are discrete to begin with (evenly spaced samples in the time domain), so I kind of see what you're trying to say... but I don't fully understand the work yet. I'm slowly making my way, starting with getting acquainted with classic state-space models. That said, my sense is there's a significant difference between (a) learning to approximate the matrix of n×n interactions with more run-of-the-mill methods, such as some kind of linear decomposition with learned fixed coefficients, versus (b) learning to approximate the matrix of n×n interactions with dynamic methods that "find the most suitable decomposition for each sample on-the-fly," which is what these class of models appears to be doing. Apologies if all this sounds very hand-wavy; it's the best I can do at the moment.