Convolutional Mode (Training)
During training, the recurrence x_t = Āx_{t-1} + B̄u_t can be unrolled and rearranged as a convolution: output = conv(input, kernel).
During training, the recurrence x_t = Āx_{t-1} + B̄u_t can be unrolled and rearranged as a convolution: output = conv(input, kernel). The kernel is determined by powers of A: [A^0, A^1, A^2, ...]. Can be computed via FFT in O(n log n) time and parallelized.