Sharp Approximation Rates for Neural Networks with Affine Latent Parameterizations
The result shows that even a fixed-dimensional latent space suffices to achieve vanishing approximation error as the network budget increases, and it is proved that the optimal worst-case uniform approximation error over the unit ball ofolder functions on $[0,1]^d$ has the sharp order.
Shi-Jun Zhang
· 0 citations