Optimal Tradeoffs Between Network Size and Parameter Magnitude in Neural Approximation and Minimax Regression

The statistical accuracy of neural networks depends on both their approximation power and the complexity of the class fitted from data. While increasing network size is a natural way to improve approximation, parameter magnitude provides another resource whose role must be quantified in both respect