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I guess it is mostly an illustration that time-domain => frequency-domain transformation is trivial for ANNs. And e.g. it won't make any sense to pre-process yo
by killerstorm 5y ago
I guess it is mostly an illustration that time-domain => frequency-domain transformation is trivial for ANNs. And e.g. it won't make any sense to pre-process your signal with FFT before training ANN since it will do it itself if needed.
- raphaelj 5y agoWell, you can still probably reduce the training computational cost by feeding the additional FFT features to your NN.
- ellisv 5y agoThe Fourier transform layer might not be the immediate hidden layer.
- ellisv 5y agoThat's a fair point. My assumption was that the neural network isn't going to be as efficient at the FFT but the article doesn't include that type of assessment.
- killerstorm 5y agoNN is doing matrix multiplications anyway. That's what it does, basically. The property of matrix multiplications is that they are composable, i.e. `X * (Y * z) = (X * Y) * z` that is, in the end you only need one matrix. So what this means in practice is that you have FFT for free. NN is doing a matrix multiplication anyway. Discrete time Fourier Transform is a matrix multiplication. Thus it can simply fold DTFT together with whatever other transform it is doing - it doesn't cost anything.