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>especially in convolutional/correlation neural networks, which often make use of the convolution theorem to do the convolution

Is this true? With the learned filters being so much smaller than the input imagery/signals, and with "striding" operations and different boundary conditions being wrapped into these algorithms, it doesn't seem like a natural fit.



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