1D convolutions are often applied at the earliest layers when doing deep learning on dependent (time-series, dynamical, audio e.g. WaveNet, etc) data. Are there any best practices for interpreting the learned convolution weights? Can anyone point me to examples of good papers where the representations learned by stacked 1D-convolutions were interpreted? A signal-processing (or other established mathematical framework’s) viewpoint would be especially interesting. Thanks!