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Error bounds for approximations with deep ReLU networks

v1v2v3 (latest)

Error bounds for approximations with deep ReLU networks

3 October 2016

Dmitry Yarotsky

ArXiv (abs)PDF HTML

Papers citing "Error bounds for approximations with deep ReLU networks"

50 / 633 papers shown

Upper Approximation Bounds for Neural Oscillators

Upper Approximation Bounds for Neural Oscillators

Konstantin M. Zuev

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0

30 Nov 2025

A Quantifier-Reversal Approximation Paradigm for Recurrent Neural Networks

A Quantifier-Reversal Approximation Paradigm for Recurrent Neural Networks

Valentin Abadie

Helmut Bölcskei

120

0

0

19 Nov 2025

Convomem Benchmark: Why Your First 150 Conversations Don't Need RAG

Convomem Benchmark: Why Your First 150 Conversations Don't Need RAGJournal of the mechanics and physics of solids (JMPS), 2025

299

0

0

13 Nov 2025

Deep Neural Operator Learning for Probabilistic Models

Deep Neural Operator Learning for Probabilistic Models

Erhan Bayraktar

124

1

0

10 Nov 2025

One model to solve them all: 2BSDE families via neural operators

One model to solve them all: 2BSDE families via neural operators

Anastasis Kratsios

Dylan Possamaï

149

0

0

03 Nov 2025

Bayesian Neural Networks vs. Mixture Density Networks: Theoretical and Empirical Insights for Uncertainty-Aware Nonlinear Modeling

Bayesian Neural Networks vs. Mixture Density Networks: Theoretical and Empirical Insights for Uncertainty-Aware Nonlinear Modeling

389

0

0

28 Oct 2025

Neural Mutual Information Estimation with Vector Copulas

Neural Mutual Information Estimation with Vector Copulas

Michael U. Gutmann

148

2

0

23 Oct 2025

Neural Networks for Censored Expectile Regression Based on Data Augmentation

Neural Networks for Censored Expectile Regression Based on Data Augmentation

103

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0

23 Oct 2025

On Universality of Deep Equivariant Networks

On Universality of Deep Equivariant Networks

Mircea Petrache

Shubhendu Trivedi

101

1

0

17 Oct 2025

In-Context Learning Is Provably Bayesian Inference: A Generalization Theory for Meta-Learning

In-Context Learning Is Provably Bayesian Inference: A Generalization Theory for Meta-Learning

Tomoya Wakayama

375

3

0

13 Oct 2025

A Mathematical Explanation of Transformers for Large Language Models and GPTs

A Mathematical Explanation of Transformers for Large Language Models and GPTs

Raymond H. F. Chan

159

1

0

05 Oct 2025

Sharp Lower Bounds for Linearized ReLU^k Approximation on the Sphere

Sharp Lower Bounds for Linearized ReLU^k Approximation on the Sphere

165

0

0

05 Oct 2025

Sparse Deep Additive Model with Interactions: Enhancing Interpretability and Predictability

Sparse Deep Additive Model with Interactions: Enhancing Interpretability and Predictability

Vince D. Calhoun

117

0

0

27 Sep 2025

Statistical Learning Guarantees for Group-Invariant Barron Functions

Statistical Learning Guarantees for Group-Invariant Barron Functions

118

1

0

27 Sep 2025

Generalization Analysis for Classification on Korobov Space

Generalization Analysis for Classification on Korobov Space

89

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0

26 Sep 2025

KANO: Kolmogorov-Arnold Neural Operator

KANO: Kolmogorov-Arnold Neural Operator

Murphy Yuezhen Niu

188

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0

20 Sep 2025

Circuit realization and hardware linearization of monotone operator equilibrium networks

Circuit realization and hardware linearization of monotone operator equilibrium networks

112

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0

17 Sep 2025

Expressive Power of Deep Networks on Manifolds: Simultaneous Approximation

Expressive Power of Deep Networks on Manifolds: Simultaneous Approximation

186

0

0

11 Sep 2025

Information-Theoretic Bounds and Task-Centric Learning Complexity for Real-World Dynamic Nonlinear Systems

Information-Theoretic Bounds and Task-Centric Learning Complexity for Real-World Dynamic Nonlinear Systems

Sri Satish Krishna Chaitanya Bulusu

Mikko Sillanpää

135

1

0

08 Sep 2025

Distribution estimation via Flow Matching with Lipschitz guarantees

Distribution estimation via Flow Matching with Lipschitz guarantees

130

2

0

02 Sep 2025

Beyond Universal Approximation Theorems: Algorithmic Uniform Approximation by Neural Networks Trained with Noisy Data

Beyond Universal Approximation Theorems: Algorithmic Uniform Approximation by Neural Networks Trained with Noisy Data

Anastasis Kratsios

173

0

0

31 Aug 2025

Theory Foundation of Physics-Enhanced Residual Learning

Theory Foundation of Physics-Enhanced Residual Learning

136

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0

30 Aug 2025

Objective Value Change and Shape-Based Accelerated Optimization for the Neural Network Approximation

Objective Value Change and Shape-Based Accelerated Optimization for the Neural Network Approximation

125

0

0

27 Aug 2025

Factor Informed Double Deep Learning For Average Treatment Effect Estimation

Factor Informed Double Deep Learning For Average Treatment Effect Estimation

Sanjeev R. Kulkarni

121

1

0

23 Aug 2025

Error analysis for the deep Kolmogorov method

Error analysis for the deep Kolmogorov method

Iulian Cîmpean

116

0

0

23 Aug 2025

Deep Learning for Markov Chains: Lyapunov Functions, Poisson's Equation, and Stationary Distributions

Deep Learning for Markov Chains: Lyapunov Functions, Poisson's Equation, and Stationary Distributions

100

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0

22 Aug 2025

On Task Vectors and Gradients

On Task Vectors and Gradients

Daniele Solombrino

Donato Crisostomi

Maria Sofia Bucarelli

Giuseppe Alessio D’Inverno

Fabrizio Silvestri

Emanuele Rodolà

420

1

0

22 Aug 2025

Neural Networks with Orthogonal Jacobian

Neural Networks with Orthogonal Jacobian

Carola-Bibiane Schönlieb

201

1

0

04 Aug 2025

A Contrastive Diffusion-based Network (CDNet) for Time Series Classification

A Contrastive Diffusion-based Network (CDNet) for Time Series Classification

145

1

0

28 Jul 2025

Universal Fourier Neural Operators for periodic homogenization problems in linear elasticity

Universal Fourier Neural Operators for periodic homogenization problems in linear elasticityJournal of the mechanics and physics of solids (JMPS), 2025

Binh Huy Nguyen

Matti Schneider

254

0

0

16 Jul 2025

When do World Models Successfully Learn Dynamical Systems?

When do World Models Successfully Learn Dynamical Systems?

Claudia Drygala

Leonhard Schwarz

Hanno Gottschalk

141

0

0

07 Jul 2025

Bridging Unsupervised and Semi-Supervised Anomaly Detection: A Theoretically-Grounded and Practical Framework with Synthetic Anomalies

Bridging Unsupervised and Semi-Supervised Anomaly Detection: A Theoretically-Grounded and Practical Framework with Synthetic Anomalies

212

0

0

16 Jun 2025

On Universality Classes of Equivariant Networks

On Universality Classes of Equivariant Networks

Shubhendu Trivedi

190

1

0

02 Jun 2025

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time

533

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0

23 May 2025

Efficient Adaptive Experimentation with Noncompliance

Efficient Adaptive Experimentation with Noncompliance

382

0

0

23 May 2025

Approximation theory for 1-Lipschitz ResNets

Approximation theory for 1-Lipschitz ResNets

Carola-Bibiane Schönlieb

425

4

0

17 May 2025

SAD Neural Networks: Divergent Gradient Flows and Asymptotic Optimality via o-minimal Structures

SAD Neural Networks: Divergent Gradient Flows and Asymptotic Optimality via o-minimal Structures

Steffen Dereich

231

3

0

14 May 2025

Super-fast rates of convergence for Neural Networks Classifiers under the Hard Margin Condition

Super-fast rates of convergence for Neural Networks Classifiers under the Hard Margin Condition

Nathanael Tepakbong

445

0

0

13 May 2025

Universal Approximation Theorem for Deep Q-Learning via FBSDE System

Universal Approximation Theorem for Deep Q-Learning via FBSDE System

200

2

0

09 May 2025

Transformers for Learning on Noisy and Task-Level Manifolds: Approximation and Generalization Insights

Transformers for Learning on Noisy and Task-Level Manifolds: Approximation and Generalization Insights

324

2

0

06 May 2025

Binding threshold units with artificial oscillatory neurons

Binding threshold units with artificial oscillatory neurons

245

0

0

06 May 2025

A New Perspective To Understanding Multi-resolution Hash Encoding For Neural Fields

A New Perspective To Understanding Multi-resolution Hash Encoding For Neural Fields

Steven Tin Sui Luo

244

0

0

05 May 2025

Universal Approximation Theorem of Deep Q-Networks

Universal Approximation Theorem of Deep Q-Networks

186

5

0

04 May 2025

Transformers Can Overcome the Curse of Dimensionality: A Theoretical Study from an Approximation Perspective

Transformers Can Overcome the Curse of Dimensionality: A Theoretical Study from an Approximation Perspective

249

1

0

18 Apr 2025

On the minimax optimality of Flow Matching through the connection to kernel density estimation

On the minimax optimality of Flow Matching through the connection to kernel density estimation

264

5

0

17 Apr 2025

Approximation Bounds for Transformer Networks with Application to Regression

Approximation Bounds for Transformer Networks with Application to Regression

461

1

0

16 Apr 2025

Expressivity of Quadratic Neural ODEs

Expressivity of Quadratic Neural ODEs

220

0

0

13 Apr 2025

Inference for Deep Neural Network Estimators in Generalized Nonparametric Models

Inference for Deep Neural Network Estimators in Generalized Nonparametric Models

259

0

0

12 Apr 2025

Neural network-enhanced integrators for simulating ordinary differential equations

Neural network-enhanced integrators for simulating ordinary differential equations

Kathrin Flaßkamp

147

0

0

07 Apr 2025

Nonlocal techniques for the analysis of deep ReLU neural network approximations

Nonlocal techniques for the analysis of deep ReLU neural network approximations

Cornelia Schneider

221

3

0

07 Apr 2025

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