Cover Image

Convergence Analysis of Max-Min Exponential Neural Network Operators in Orlicz Space

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Pradhan, Satyaranjan, Soren, Madan Mohan
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Functional Analysis
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

On the Convergence of Max-product and Max-Min Durrmeyer-type Exponential Sampling Operators
by: Pradhan, Satyaranjan, et al.
Published: (2025)

Weighted Approximation By Max-Product Generalized Exponential Sampling Series
by: Pradhan, Satyaranjan, et al.
Published: (2024)

Weighted approximation By Max-product Kantrovich type Exponential Sampling Series
by: Pradhan, Satyaranjan, et al.
Published: (2025)

Convergence analysis of max-product and max-min Durrmeyer-type exponential sampling operators in Mellin Orlicz space
by: Pradhan, Satyaranjan, et al.
Published: (2025)

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

Approximation by Steklov Neural Network Operators
by: Karaman, S. N., et al.
Published: (2024)

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

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

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

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

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

Strong and weak sharp bounds for Neural Network Operators in Sobolev-Orlicz spaces and their quantitative extensions to Orlicz spaces
by: Costarelli, Danilo, et al.
Published: (2025)

Orlicz Space Interpolation and Its Applications to Operator Convolution
by: Bauer, Wolfram, et al.
Published: (2026)

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

The Function Representation of Artificial Neural Network
by: Ma, Zhongkui
Published: (2019)

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

Fourier Multipliers on Quasi-Banach Orlicz Spaces and Orlicz Modulation Spaces
by: Petersson, Albin
Published: (2025)

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

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

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

Gohberg-Krupnik Localisation for Discrete Wiener-Hopf Operators on Orlicz Sequence Spaces
by: Karlovych, Oleksiy, et al.
Published: (2025)

Hypothesis Spaces for Deep Learning
by: Wang, Rui, et al.
Published: (2024)

Learning Orthonormal Bases for Function Spaces
by: Kamkari, Hamidreza, et al.
Published: (2026)

Convergent Stochastic Training of Attention and Understanding LoRA
by: Sun, Zhengkai, et al.
Published: (2026)

Sparse Representer Theorems for Learning in Reproducing Kernel Banach Spaces
by: Wang, Rui, et al.
Published: (2023)

Neural networks in non-metric spaces
by: Galimberti, Luca
Published: (2024)

Featured Reproducing Kernel Banach Spaces for Learning and Neural Networks
by: de la Higuera, Isabel, et al.
Published: (2026)

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

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

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

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

Spherical Analysis of Learning Nonlinear Functionals
by: Yang, Zhenyu, et al.
Published: (2024)

On the Uniqueness of Best Non-decreasing Approximation in Orlicz Spaces
by: Benavente, Ana, et al.
Published: (2025)

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

Noisy Data Visualization using Functional Data Analysis
by: Chen, Haozhe, et al.
Published: (2024)

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

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

Gaussian Processes with Sample Paths in Reproducing Kernel Banach Spaces
by: Karvonen, Toni, et al.
Published: (2026)

Skew von Neumann constant in Weak Orlicz Spaces and Weak Lebesgue Spaces
by: Zhou, Haoyu, et al.
Published: (2025)

Quantum Harmonic Analysis and the Structure in Data: Augmentation
by: Doerfler, Monika, et al.
Published: (2025)