Exponential ReLU DNN Expression of Holomorphic Maps in High Dimension

نویسندگان

چکیده

Abstract For a parameter dimension $$d\in {\mathbb {N}}$$ d ? N , we consider the approximation of many-parametric maps $$u: [-\,1,1]^d\rightarrow R}$$ u : [ - 1 , ] ? R by deep ReLU neural networks. The input d may possibly be large, and assume quantitative control domain holomorphy u : i.e., admits holomorphic extension to Bernstein polyellipse $${{\mathcal {E}}}_{\rho _1}\times \cdots \times {{\mathcal _d} \subset {C}}^d$$ E ? × ? ? C semiaxis sums $$\rho _i>1$$ i > containing $$[-\,1,1]^{d}$$ . We establish exponential rate $$O(\exp (-\,bN^{1/(d+1)}))$$ O ( exp b / + ) expressive power in terms total NN size N $$W^{1,\infty }([-\,1,1]^d)$$ W ? constant $$b>0$$ 0 depends on $$(\rho _j)_{j=1}^d$$ j = which characterizes coordinate-wise sizes Bernstein-ellipses for also prove convergence stronger norms DNNs with more regular, so-called “rectified unit” activations. Finally, extend DNN expression bounds two classes non-holomorphic functions, particular -variate, Gevrey-regular and, composition, certain multivariate probability distribution functions Lipschitz marginals.

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ژورنال

عنوان ژورنال: Constructive Approximation

سال: 2021

ISSN: ['0176-4276', '1432-0940']

DOI: https://doi.org/10.1007/s00365-021-09542-5