Copertina

Approximation by Steklov Neural Network Operators

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Karaman, S. N., Turgay, M., Acar, T.
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Functional Analysis Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Theory-to-Practice Gap for Neural Networks and Neural Operators
di: Grohs, Philipp, et al.
Pubblicazione: (2025)

Quantitative Sobolev Approximation Bounds for Neural Operators with Empirical Validation on Burgers Equation
di: Hao, Nicole
Pubblicazione: (2026)

Convergence Analysis of Max-Min Exponential Neural Network Operators in Orlicz Space
di: Pradhan, Satyaranjan, et al.
Pubblicazione: (2025)

Parseval Convolution Operators and Neural Networks
di: Unser, Michael, et al.
Pubblicazione: (2024)

Non-Asymptotic Stability and Consistency Guarantees for Physics-Informed Neural Networks via Coercive Operator Analysis
di: Katende, Ronald
Pubblicazione: (2025)

Upper Approximation Bounds for Neural Oscillators
di: Huang, Zifeng, et al.
Pubblicazione: (2025)

Bridging Smoothness and Approximation: Theoretical Insights into Over-Smoothing in Graph Neural Networks
di: Yang, Guangrui, et al.
Pubblicazione: (2024)

How Analysis Can Teach Us the Optimal Way to Design Neural Operators
di: Le, Vu-Anh, et al.
Pubblicazione: (2024)

The Function Representation of Artificial Neural Network
di: Ma, Zhongkui
Pubblicazione: (2019)

A Kernel-based Stochastic Approximation Framework for Nonlinear Operator Learning
di: Yang, Jia-Qi, et al.
Pubblicazione: (2025)

Approximation Properties of Mellin-Steklov Type Exponential Sampling Series
di: Ozer, D, et al.
Pubblicazione: (2024)

Universal Approximation of Nonlinear Operators and Their Derivatives
di: de Feo, Filippo
Pubblicazione: (2026)

Controlled Learning of Pointwise Nonlinearities in Neural-Network-Like Architectures
di: Unser, Michael, et al.
Pubblicazione: (2024)

Horizon Activation Mapping for Neural Networks in Time Series Forecasting
di: Hans, Krupakar, et al.
Pubblicazione: (2026)

Approximation of Permutation Invariant Polynomials by Transformers: Efficient Construction in Column-Size
di: Takeshita, Naoki, et al.
Pubblicazione: (2025)

Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning
di: Berner, Julius, et al.
Pubblicazione: (2025)

Neural networks in non-metric spaces
di: Galimberti, Luca
Pubblicazione: (2024)

A Multiplicative Neural Network Architecture: Locality and Regularity of Approximation
di: Choi, Hee-Sun, et al.
Pubblicazione: (2026)

A Fractional Fox H-Function Kernel for Support Vector Machines: Robust Classification via Weighted Transmutation Operators
di: Dorrego, Gustavo
Pubblicazione: (2026)

Approximation by Neural Network Sampling Operators in Mixed Lebesgue Spaces
di: Dey, Arpan Kumar, et al.
Pubblicazione: (2025)

Neural reproducing kernel Banach spaces and representer theorems for deep networks
di: Bartolucci, Francesca, et al.
Pubblicazione: (2024)

Approximation Rates in Besov Norms and Sample-Complexity of Kolmogorov-Arnold Networks with Residual Connections
di: Kratsios, Anastasis, et al.
Pubblicazione: (2025)

Learning Operators by Regularized Stochastic Gradient Descent with Operator-valued Kernels
di: Yang, Jia-Qi, et al.
Pubblicazione: (2025)

Kantorovich--Kernel Neural Operators: Approximation Theory, Asymptotics, and Neural Network Interpretation
di: He, Tian-Xiao
Pubblicazione: (2026)

Why High-rank Neural Networks Generalize?: An Algebraic Framework with RKHSs
di: Hashimoto, Yuka, et al.
Pubblicazione: (2025)

Neural Hilbert Ladders: Multi-Layer Neural Networks in Function Space
di: Chen, Zhengdao
Pubblicazione: (2023)

Sparse-Aware Neural Networks for Nonlinear Functionals: Mitigating the Exponential Dependence on Dimension
di: Li, Jianfei, et al.
Pubblicazione: (2026)

Rigorous Error Certification for Neural PDE Solvers: From Empirical Residuals to Solution Guarantees
di: Mukherjee, Amartya, et al.
Pubblicazione: (2026)

Noncommutative $C^*$-algebra Net: Learning Neural Networks with Powerful Product Structure in $C^*$-algebra
di: Hataya, Ryuichiro, et al.
Pubblicazione: (2023)

Prompting a Pretrained Transformer Can Be a Universal Approximator
di: Petrov, Aleksandar, et al.
Pubblicazione: (2024)

Approximation analysis of CNNs from a feature extraction view
di: Li, Jianfei, et al.
Pubblicazione: (2022)

Do stable neural networks exist for classification problems? -- A new view on stability in AI
di: Liu, Z. N. D., et al.
Pubblicazione: (2024)

Understanding Transfer Learning via Mean-field Analysis
di: Aminian, Gholamali, et al.
Pubblicazione: (2024)

Learning Operators with Stochastic Gradient Descent in General Hilbert Spaces
di: Shi, Lei, et al.
Pubblicazione: (2024)

An Interpretable Approach to Load Profile Forecasting in Power Grids using Galerkin-Approximated Koopman Pseudospectra
di: Tavasoli, Ali, et al.
Pubblicazione: (2023)

Orthogonal Polynomials Approximation Algorithm (OPAA):a functional analytic approach to estimating probability densities
di: Bialokozowicz, Lilian W.
Pubblicazione: (2022)

Stable Learning Using Spiking Neural Networks Equipped With Affine Encoders and Decoders
di: Neuman, A. Martina, et al.
Pubblicazione: (2024)

Langevin Monte-Carlo Provably Learns Depth Two Neural Nets at Any Size and Data
di: Kumar, Dibyakanti, et al.
Pubblicazione: (2025)

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

Constructive Approximation under Carleman's Condition, with Applications to Smoothed Analysis
di: Koehler, Frederic, et al.
Pubblicazione: (2025)