:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Yang, Xiao-Song, Zhou, Qi, Zhou, Xuan
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2511.06837
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

Universal Approximation Theorem of Deep Q-Networks
by: Qi, Qian
Published: (2025)

Relocation of compact sets in $\mathbb{R}^n$ by diffeomorphisms and linear separability of datasets in $\mathbb{R}^n$
by: Yang, Xiao-Song, et al.
Published: (2026)

Constructive Universal Approximation and Finite Sample Memorization by Narrow Deep ReLU Networks
by: Hernández, Martín, et al.
Published: (2024)

Virtual Width Networks
by: Seed, et al.
Published: (2025)

Expressive Power of Deep Networks on Manifolds: Simultaneous Approximation
by: Zhou, Hanfei, et al.
Published: (2025)

Classification with Deep Neural Networks and Logistic Loss
by: Zhang, Zihan, et al.
Published: (2023)

Training Deep Morphological Neural Networks as Universal Approximators
by: Fotopoulos, Konstantinos, et al.
Published: (2025)

Two-Dimensional Deep ReLU CNN Approximation for Korobov Functions: A Constructive Approach
by: Fang, Qin, et al.
Published: (2025)

Optimal Convergence Rates of Deep Neural Network Classifiers
by: Zhang, Zihan, et al.
Published: (2025)

Geometric Limits of Knowledge Distillation: A Minimum-Width Theorem via Superposition Theory
by: Sarkar, Nilesh, et al.
Published: (2026)

Universal Approximation Theorem for Deep Q-Learning via FBSDE System
by: Qi, Qian
Published: (2025)

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

Loss Gradient Gaussian Width based Generalization and Optimization Guarantees
by: Banerjee, Arindam, et al.
Published: (2024)

Universal Approximation Constraints of Narrow ResNets: The Tunnel Effect
by: Kuehn, Christian, et al.
Published: (2026)

Adaptive Width Neural Networks
by: Errica, Federico, et al.
Published: (2025)

Mixture of Universal Experts: Scaling Virtual Width via Depth-Width Transformation
by: Chen, Yilong, et al.
Published: (2026)

An Infinite-Width Analysis on the Jacobian-Regularised Training of a Neural Network
by: Kim, Taeyoung, et al.
Published: (2023)

Progressive Approximation in Deep Residual Networks: Theory and Validation
by: Wang, Wei, et al.
Published: (2026)

Network Anomaly Traffic Detection via Multi-view Feature Fusion
by: Hao, Song, et al.
Published: (2024)

The Median is Easier than it Looks: Approximation with a Constant-Depth, Linear-Width ReLU Network
by: Dutta, Abhigyan, et al.
Published: (2026)

Towards Cheaper Inference in Deep Networks with Lower Bit-Width Accumulators
by: Blumenfeld, Yaniv, et al.
Published: (2024)

Channel Estimation by Infinite Width Convolutional Networks
by: Mallik, Mohammed, et al.
Published: (2025)

The Role of Depth, Width, and Tree Size in Expressiveness of Deep Forest
by: Lyu, Shen-Huan, et al.
Published: (2024)

Sobolev Approximation of Deep ReLU Networks in Log-Barron Space
by: Song, Changhoon, et al.
Published: (2026)

Quantitative Flow Approximation Properties of Narrow Neural ODEs
by: Elamvazhuthi, Karthik
Published: (2025)

Towards Narrowing the Generalization Gap in Deep Boolean Networks
by: Kim, Youngsung
Published: (2024)

Flexible Infinite-Width Graph Convolutional Neural Networks
by: Anson, Ben, et al.
Published: (2024)

Infinite Width Limits of Self Supervised Neural Networks
by: Fleissner, Maximilian, et al.
Published: (2024)

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

Neural Networks With Dense Weights Are Not Universal Approximators
by: Rauchwerger, Levi, et al.
Published: (2026)

Finite-Dimensional Gaussian Approximation for Deep Neural Networks: Universality in Random Weights
by: Balasubramanian, Krishnakumar, et al.
Published: (2025)

Depth Separation in Norm-Bounded Infinite-Width Neural Networks
by: Parkinson, Suzanna, et al.
Published: (2024)

Parsimonious Universal Function Approximator for Elastic and Elasto-Plastic Cavity Expansion Problems
by: Chen, Xiao-Xuan, et al.
Published: (2024)

Approximation of RKHS Functionals by Neural Networks
by: Zhou, Tian-Yi, et al.
Published: (2024)

Optimal rates of approximation by shallow ReLU$^k$ neural networks and applications to nonparametric regression
by: Yang, Yunfei, et al.
Published: (2023)

Convergence Analysis for Deep Sparse Coding via Convolutional Neural Networks
by: Li, Jianfei, et al.
Published: (2024)

On Universality of Non-Separable Approximate Message Passing Algorithms
by: Lovig, Max, et al.
Published: (2025)

Statistical Physics of Deep Neural Networks: Generalization Capability, Beyond the Infinite Width, and Feature Learning
by: Ariosto, Sebastiano
Published: (2025)

Sufficient Conditions for Stability of Minimum-Norm Interpolating Deep ReLU Networks
by: Harzli, Ouns El, et al.
Published: (2026)