  Characterizing the remarkable generalization properties of over-parameterized
neural networks remains an open problem. In this paper, we promote a shift of
focus towards initialization rather than neural architecture or (stochastic)
gradient descent to explain this implicit regularization. Through a Fourier
lens, we derive a general result for the spectral bias of neural networks and
show that the generalization of neural networks is heavily tied to their
initialization. Further, we empirically solidify the developed theoretical
insights using practical, deep networks. Finally, we make a case against the
controversial flat-minima conjecture and show that Fourier analysis grants a
more reliable framework for understanding the generalization of neural
networks.
