Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev and Besov Spaces
This solves a foundational problem in approximation theory for neural networks, with implications for scientific computing and signal processing.
The paper tackled the problem of approximating functions in Sobolev and Besov spaces using deep ReLU neural networks, providing a complete solution for all parameter ranges and showing that deep networks outperform classical methods in parameter efficiency, though with non-encodable parameters.
Let $Ω= [0,1]^d$ be the unit cube in $\mathbb{R}^d$. We study the problem of how efficiently, in terms of the number of parameters, deep neural networks with the ReLU activation function can approximate functions in the Sobolev spaces $W^s(L_q(Ω))$ and Besov spaces $B^s_r(L_q(Ω))$, with error measured in the $L_p(Ω)$ norm. This problem is important when studying the application of neural networks in a variety of fields, including scientific computing and signal processing, and has previously been solved only when $p=q=\infty$. Our contribution is to provide a complete solution for all $1\leq p,q\leq \infty$ and $s > 0$ for which the corresponding Sobolev or Besov space compactly embeds into $L_p$. The key technical tool is a novel bit-extraction technique which gives an optimal encoding of sparse vectors. This enables us to obtain sharp upper bounds in the non-linear regime where $p > q$. We also provide a novel method for deriving $L_p$-approximation lower bounds based upon VC-dimension when $p < \infty$. Our results show that very deep ReLU networks significantly outperform classical methods of approximation in terms of the number of parameters, but that this comes at the cost of parameters which are not encodable.