Cover Image

Nonlocal techniques for the analysis of deep ReLU neural network approximations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Schneider, Cornelia, Ullrich, Mario, Vybiral, Jan
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Computational Complexity Numerical Analysis 68T07, 42C15, 11A25
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

ReLU neural network approximation to piecewise constant functions
by: Cai, Zhiqiang, et al.
Published: (2024)

On the algorithmic construction of deep ReLU networks
by: Huybrechs, Daan
Published: (2025)

Subspace method based on neural networks for eigenvalue problems
by: Dai, Xiaoying, et al.
Published: (2024)

Deterministic constructions of high-dimensional sets with small dispersion
by: Ullrich, Mario, et al.
Published: (2019)

Equidistribution-based training of Free Knot Splines and ReLU Neural Networks
by: Appella, Simone, et al.
Published: (2024)

Spectral Barron space for deep neural network approximation
by: Liao, Yulei, et al.
Published: (2023)

On the existence of minimizers in shallow residual ReLU neural network optimization landscapes
by: Dereich, Steffen, et al.
Published: (2023)

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for space-time solutions of semilinear partial differential equations
by: Ackermann, Julia, et al.
Published: (2024)

Approximation bounds for norm constrained deep neural networks
by: Maiale, Francesco Paolo, et al.
Published: (2025)

Deep neural networks with ReLU, leaky ReLU, and softplus activation provably overcome the curse of dimensionality for Kolmogorov partial differential equations with Lipschitz nonlinearities in the $L^p$-sense
by: Ackermann, Julia, et al.
Published: (2023)

Pay Attention to Attention Distribution: A New Local Lipschitz Bound for Transformers
by: Yudin, Nikolay, et al.
Published: (2025)

A posteriori certification for neural network approximations to PDEs
by: Ernst, Lewin, et al.
Published: (2025)

On the existence of optimal shallow feedforward networks with ReLU activation
by: Dereich, Steffen, et al.
Published: (2023)

Exact Loop Controllers for ReLU Realization of Homogeneous Curve Refinements
by: Bolorkhuu, Boldsaikhan, et al.
Published: (2026)

On the power of iid information for linear approximation
by: Sonnleitner, Mathias, et al.
Published: (2023)

On the growth of the parameters of approximating ReLU neural networks
by: Morina, Erion, et al.
Published: (2024)

The minimal width of universal $p$-adic ReLU neural networks
by: Kiss, Sándor Z., et al.
Published: (2026)

Operator Inference for Elliptic Eigenvalue Problems
by: Li, Haoqian, et al.
Published: (2025)

Error analysis for empirical risk minimization over clipped ReLU networks in solving linear Kolmogorov partial differential equations
by: Xiao, Jichang, et al.
Published: (2023)

Matrix perturbation analysis of methods for extracting singular values from approximate singular subspaces
by: Lazzarino, Lorenzo, et al.
Published: (2024)

Regularity Analysis and Tensor Neural Network Methods for Quasiperiodic Elliptic Equations
by: Ren, Jingze, et al.
Published: (2026)

FieldTNN-based machine learning method for Maxwell eigenvalue problems
by: Jiang, Jiantao, et al.
Published: (2024)

An algorithmic approach to direct spline products: procedures and computational aspects
by: Patrizi, Francesco, et al.
Published: (2026)

Finite-difference least square methods for solving Hamilton-Jacobi equations using neural networks
by: Esteve-Yagüe, Carlos, et al.
Published: (2024)

Certified and accurate computation of function space norms of deep neural networks
by: Gründler, Johannes, et al.
Published: (2026)

Conservative approximation-based feedforward neural network for WENO schemes
by: Park, Kwanghyuk, et al.
Published: (2025)

Near-optimal estimates for the $\ell^p$-Lipschitz constants of deep random ReLU neural networks
by: Dirksen, Sjoerd, et al.
Published: (2025)

Accurately recover global quasiperiodic systems by finite points
by: Jiang, Kai, et al.
Published: (2023)

Quantitative analysis for $L^2$-estimates in linear elliptic equations via divergence-free transformation
by: Lee, Haesung
Published: (2025)

Neural semi-Lagrangian method for high-dimensional advection-diffusion problems
by: Franck, Emmanuel, et al.
Published: (2025)

Minimum Width of Leaky-ReLU Neural Networks for Uniform Universal Approximation
by: Li, Li'ang, et al.
Published: (2023)

A PINNs approach for the computation of eigenvalues in elliptic problems
by: Bonder, Julian Fernandez, et al.
Published: (2025)

Sharp error bounds for approximate eigenvalues and singular values from subspace methods
by: Haas, Irina-Beatrice, et al.
Published: (2025)

Hard-constrained Physics-informed Neural Networks for Interface Problems
by: Chung, Seung Whan, et al.
Published: (2026)

Quantitative Approximation Rates for Group Equivariant Learning
by: Siegel, Jonathan W., et al.
Published: (2026)

Neural network solvers for parametrized elasticity problems that conserve linear and angular momentum
by: Boon, Wietse M., et al.
Published: (2024)

Sampling recovery in $L_2$ and other norms
by: Krieg, David, et al.
Published: (2023)

Domain-decomposed image classification algorithms using linear discriminant analysis and convolutional neural networks
by: Klawonn, Axel, et al.
Published: (2024)

Deep Neural Networks with Symplectic Preservation Properties
by: He, Qing, et al.
Published: (2024)

Expression Rates of Neural Operators for Linear Elliptic PDEs in Polytopes
by: Marcati, Carlo, et al.
Published: (2024)