A law of robustness for two-layer neural networks with arbitrary weights

arXiv:2607. 07778v1 Announce Type: new Abstract: Bubeck, Li and Nagaraj conjectured that, for generic data, any two-layer neural network with $m$ neurons that fits $n$ noisy labels must have Lipschitz constant at least of order $\sqrt{n/m}$, with no restriction on the size of the weights.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.