Oct 16, 2023 · This paper introduces deep super ReLU networks (DSRNs) as a method for approximating functions in Sobolev spaces measured by Sobolev norms W^{m, ...
In this section, we study lower bounds on the approximation rates that deep ReLU neural networks can achieve on Sobolev spaces. Our main result is to prove ...
Nov 25, 2022 · We study the problem of how efficiently, in terms of the number of parameters, deep neural networks with the ReLU activation function can ...
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Dec 28, 2023 · Abstract : This paper introduces deep super ReLU networks ... Nearly Optimal Approximation Rates for Deep Super ReLU Networks on Sobolev Spaces( ...
Nearly Optimal Approximation Rates for Deep Super ReLU Networks on Sobolev Spaces · Score-based Transport Modeling for Mean-Field Fokker-Planck Equations.
Let F be a class of functions from X to R. The pseudo-dimension of F, denoted by Pdim(F), is the largest integer m for which there exists (x1,x2,...,xm,y1 ...
Nearly optimal approximation rates for deep super relu networks on sobolev spaces ... network approximation for Korobov functions with respect to Sobolev ...
May 29, 2024 · Nearly optimal approximation rates for deep super ReLU networks on Sobolev spaces. arXiv preprint. arXiv:2310.10766, 2023. 3. Y. Yang, Y. Lu ...
Nearly Optimal Approximation Rates for Deep Super ReLU Networks on Sobolev Spaces · Computer Science, Mathematics. ArXiv · 2023.
This paper establishes the nearly optimal rate of approximation for deep neural networks (DNNs) when applied to Korobov functions, effectively overcoming ...