On the near optimality of the stochastic approximation of smooth functions by neural networks
From MaRDI portal
Publication:1968643
DOI10.1023/A:1018993908478zbMath0939.41013OpenAlexW1513292484MaRDI QIDQ1968643
Publication date: 21 March 2000
Published in: Advances in Computational Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1023/a:1018993908478
Related Items (21)
Voronovskaja type theorems and high-order convergence neural network operators with sigmoidal functions ⋮ Probabilistic lower bounds for approximation by shallow perceptron networks ⋮ Saturation classes for MAX-product neural network operators activated by sigmoidal functions ⋮ On sharpness of error bounds for multivariate neural network approximation ⋮ Approximation bounds for random neural networks and reservoir systems ⋮ Error bounds for approximations using multichannel deep convolutional neural networks with downsampling ⋮ Meshless methods for American option pricing through physics-informed neural networks ⋮ Solving groundwater flow equation using physics-informed neural networks ⋮ Approximation by neural networks and learning theory ⋮ Asymptotic expansion for neural network operators of the Kantorovich type and high order of approximation ⋮ Quantitative estimates involving K-functionals for neural network-type operators ⋮ A selective overview of deep learning ⋮ Linearized two-layers neural networks in high dimension ⋮ Some problems in the theory of ridge functions ⋮ Error bounds for approximations with deep ReLU networks ⋮ Convergence analysis of convex incremental neural networks ⋮ On sharpness of error bounds for univariate approximation by single hidden layer feedforward neural networks ⋮ On best approximation by ridge functions ⋮ On the approximation of functional classes equipped with a uniform measure using ridge functions ⋮ Measurement error models: from nonparametric methods to deep neural networks ⋮ On best approximation of classes by radial functions
This page was built for publication: On the near optimality of the stochastic approximation of smooth functions by neural networks
