Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev Spaces
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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 space W^s(L_q(Ω)) on a bounded domain Ω, where the error is measured in L_p(Ω). This problem is important for studying the application of neural networks in scientific computing and has previously been solved only in the case p=q=∞. Our contribution is to provide a solution for all 1≤ p,q≤∞ and s > 0. Our results show that deep ReLU networks significantly outperform classical methods of approximation, but that this comes at the cost of parameters which are not encodable.
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