  Rectified Linear Units (ReLU) seem to have displaced traditional 'smooth'
nonlinearities as activation-function-du-jour in many - but not all - deep
neural network (DNN) applications. However, nobody seems to know why. In this
article, we argue that ReLU are useful because they are ideal demodulators -
this helps them perform fast abstract learning. However, this fast learning
comes at the expense of serious nonlinear distortion products - decoy features.
We show that Parallel Dither acts to suppress the decoy features, preventing
overfitting and leaving the true features cleanly demodulated for rapid,
reliable learning.
