Shuo Huang

Norm-constrained deep networks can dodge the curse of dimensionality when learning compositional functions, unlocking efficient learning even in overparameterized regimes.

Sparsity could be the key to unlocking efficient neural networks for learning operators on infinite-dimensional function spaces, sidestepping the curse of dimensionality.