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We empirically evaluate common assumptions about neural networks that arewidely held by practitioners and theorists alike. In this work, we: (1) provethe widespread existence of suboptimal local minima in the loss landscape ofneural networks, and we use our theory to find examples; (2) show thatsmall-norm parameters are not optimal for generalization; (3) demonstrate thatResNets do not conform to wide-network theories, such as the neural tangentkernel, and that the interaction between skip connections and batchnormalization plays a role; (4) find that rank does not correlate withgeneralization or robustness in a practical setting.

Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory