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This paper proves a conjectured law of robustness for two-layer neural networks with unbounded weights, showing that a network fitting noisy data must have a Lipschitz constant at least of order sqrt(n/m), up to a logarithmic factor, for continuous piecewise-linear activations like ReLU.