Cover Image

Universal approximation theorem for neural networks with inputs from a topological vector space

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Ismailov, Vugar
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Neural and Evolutionary Computing 41A30, 41A65, 68T05
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Addressing common misinterpretations of KART and UAT in neural network literature
by: Ismailov, Vugar
Published: (2024)

Neural Networks Trained by Weight Permutation are Universal Approximators
by: Cai, Yongqiang, et al.
Published: (2024)

Sharp Lower Bounds on Interpolation by Deep ReLU Neural Networks at Irregularly Spaced Data
by: Siegel, Jonathan W.
Published: (2023)

Structure-Preserving Reconstruction of Convex Lipschitz Functionals on Hilbert Spaces from Finite Samples
by: Kratsios, Anastasis
Published: (2026)

MonoKAN: Certified Monotonic Kolmogorov-Arnold Network
by: Polo-Molina, Alejandro, et al.
Published: (2024)

Efficient Approximation to Analytic and $L^p$ functions by Height-Augmented ReLU Networks
by: Li, ZeYu, et al.
Published: (2026)

Deep Neural Networks: A Formulation Via Non-Archimedean Analysis
by: Zúñiga-Galindo, W. A.
Published: (2024)

A General Weighting Theory for Ensemble Learning: Beyond Variance Reduction via Spectral and Geometric Structure
by: Fokoué, Ernest
Published: (2025)

Deep ReLU networks and high-order finite element methods II: Chebyshev emulation
by: Opschoor, Joost A. A., et al.
Published: (2023)

Optimal Transport-inspired Deep Learning Framework for Slow-Decaying Kolmogorov n-width Problems: Exploiting Sinkhorn Loss and Wasserstein Kernel
by: Khamlich, Moaad, et al.
Published: (2023)

Universality of shallow and deep neural networks on non-Euclidean spaces
by: Ismailov, Vugar
Published: (2026)

Neural Network Operator-Based Fractal Approximation: Smoothness Preservation and Convergence Analysis
by: Bhat, Aaqib Ayoub, et al.
Published: (2025)

Convergence analysis of Hermite approximations for analytic functions
by: Wang, Haiyong, et al.
Published: (2023)

Quasi-interpolation for high-dimensional function approximation
by: Gao, Wenwu, et al.
Published: (2024)

Topological DeepONets and a generalization of the Chen-Chen operator approximation theorem
by: Ismailov, Vugar
Published: (2026)

Random Projection Neural Networks of Best Approximation: Convergence theory and practical applications
by: Fabiani, Gianluca
Published: (2024)

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

On best approximation by multivariate ridge functions with applications to generalized translation networks
by: Geuchen, Paul, et al.
Published: (2024)

Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations
by: Chen, Shuang, et al.
Published: (2026)

Approximation Error and Complexity Bounds for ReLU Networks on Low-Regular Function Spaces
by: Davis, Owen, et al.
Published: (2024)

Beyond Universal Approximation Theorems: Algorithmic Uniform Approximation by Neural Networks Trained with Noisy Data
by: Kratsios, Anastasis, et al.
Published: (2025)

Compression using Quasi-Interpolation
by: Buhmann, Martin, et al.
Published: (2024)

On finite-dimensional encoding/decoding theorems for neural operators
by: Oliveira, Vinícius Luz, et al.
Published: (2026)

On Fibonacci Ensembles: An Alternative Approach to Ensemble Learning Inspired by the Timeless Architecture of the Golden Ratio
by: Fokoué, Ernest
Published: (2025)

Higher-Order Transformer Derivative Estimates for Explicit Pathwise Learning Guarantees
by: Limmer, Yannick, et al.
Published: (2024)

Transformers Can Solve Non-Linear and Non-Markovian Filtering Problems in Continuous Time For Conditionally Gaussian Signals
by: Horvath, Blanka, et al.
Published: (2023)

Time-Frequency Analysis for Neural Networks
by: Abdeljawad, Ahmed, et al.
Published: (2025)

On Leaky-Integrate-and Fire as Spike-Train-Quantization Operator on Dirac-Superimposed Continuous-Time Signals
by: Moser, Bernhard A., et al.
Published: (2024)

Classification with neural networks with quadratic decision functions
by: Frischauf, Leon, et al.
Published: (2024)

Bridging the Gap Between Approximation and Learning via Optimal Approximation by ReLU MLPs of Maximal Regularity
by: Hong, Ruiyang, et al.
Published: (2024)

A Universal Approximation Theorem for Neural Networks with Outputs in Locally Convex Spaces
by: Saini, Sachin
Published: (2026)

Weights initialization of neural networks for function approximation
by: Hu, Xinwen, et al.
Published: (2025)

Embedding Dimension Lower Bounds for Universality of Deep Sets and Janossy Pooling
by: Syed, Ali, et al.
Published: (2026)

Randomized least-squares with minimal oversampling and interpolation in general spaces
by: Chkifa, Abdellah, et al.
Published: (2023)

Necessary and sufficient conditions for universality of Kolmogorov-Arnold networks
by: Ismailov, Vugar
Published: (2026)

Neural empirical interpolation method for nonlinear model reduction
by: Hirsch, Max, et al.
Published: (2024)

Simultaneous spatial-parametric collocation approximation for parametric PDEs with log-normal random inputs
by: Dũng, Dinh
Published: (2025)

Universal Approximation Theorem for Input-Connected Multilayer Perceptrons
by: Ismailov, Vugar
Published: (2026)

Weighted Sobolev Approximation Rates for Neural Networks on Unbounded Domains
by: Abdeljawad, Ahmed, et al.
Published: (2024)

The Fekete problem in segmental polynomial interpolation
by: Bruno, Ludovico Bruni, et al.
Published: (2024)