Baicheng Li

A sharp width--magnitude tradeoff at fixed depth is established using one elementary bounded $1$-Lipschitz Dyadic--Triangular Activation and yields a continuum of statistically optimal choices, ranging from unit parameter radius to fixed network size.

Fixed-size neural networks can achieve arbitrary Sobolev approximation accuracy with new activation functions, challenging traditional limits on network size and complexity.