Any width-m two-layer piecewise-linear network with arbitrary weights that fits n noisy labels below the noise floor has Lip ≳ ε sqrt(n/(m log(m n d/ε))) with high probability on the sphere or Gaussian.
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A law of robustness for two-layer neural networks with arbitrary weights
Any width-m two-layer piecewise-linear network with arbitrary weights that fits n noisy labels below the noise floor has Lip ≳ ε sqrt(n/(m log(m n d/ε))) with high probability on the sphere or Gaussian.