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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)PDFHTML

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

50 / 633 papers shown
A Mathematical Guide to Operator Learning
A Mathematical Guide to Operator Learning
Nicolas Boullé
Alex Townsend
300
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Deep Neural Networks and Finite Elements of Any Order on Arbitrary
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Deep Neural Networks and Finite Elements of Any Order on Arbitrary Dimensions
Juncai He
Jinchao Xu
364
12
0
21 Dec 2023
Sampling Complexity of Deep Approximation Spaces
Sampling Complexity of Deep Approximation Spaces
Ahmed Abdeljawad
Philipp Grohs
175
3
0
20 Dec 2023
Statistical learning by sparse deep neural networks
Statistical learning by sparse deep neural networks
Felix Abramovich
BDL
220
1
0
15 Nov 2023
A statistical perspective on algorithm unrolling models for inverse
  problems
A statistical perspective on algorithm unrolling models for inverse problems
Yves Atchadé
Xinru Liu
Qiuyun Zhu
176
1
0
10 Nov 2023
Optimal Deep Neural Network Approximation for Korobov Functions with
  respect to Sobolev Norms
Optimal Deep Neural Network Approximation for Korobov Functions with respect to Sobolev Norms
Yahong Yang
Yulong Lu
175
4
0
08 Nov 2023
Improved weight initialization for deep and narrow feedforward neural
  network
Improved weight initialization for deep and narrow feedforward neural networkNeural Networks (Neural Netw.), 2023
Hyunwoo Lee
Yunho Kim
Seungyeop Yang
Hayoung Choi
ODL
309
15
0
07 Nov 2023
Approximating Langevin Monte Carlo with ResNet-like Neural Network
  architectures
Approximating Langevin Monte Carlo with ResNet-like Neural Network architectures
Charles Miranda
Janina Enrica Schutte
David Sommer
Martin Eigel
200
3
0
06 Nov 2023
The Evolution of the Interplay Between Input Distributions and Linear
  Regions in Networks
The Evolution of the Interplay Between Input Distributions and Linear Regions in Networks
Xuan Qi
Yi Wei
181
0
0
28 Oct 2023
Lifting the Veil: Unlocking the Power of Depth in Q-learning
Lifting the Veil: Unlocking the Power of Depth in Q-learning
Shao-Bo Lin
Tao Li
Shaojie Tang
Yao Wang
Ding-Xuan Zhou
OffRLOOD
218
0
0
27 Oct 2023
Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries
Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent GeometriesInternational Conference on Learning Representations (ICLR), 2023
Haitz Sáez de Ocáriz Borde
Anastasis Kratsios
486
6
0
23 Oct 2023
The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks
The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks
S. Bieringer
Gregor Kasieczka
Maximilian F. Steffen
Mathias Trabs
313
1
0
13 Oct 2023
Deep ReLU networks and high-order finite element methods II: Chebyshev
  emulation
Deep ReLU networks and high-order finite element methods II: Chebyshev emulationComputers and Mathematics with Applications (CMA), 2023
J. Opschoor
Christoph Schwab
287
6
0
11 Oct 2023
Residual Multi-Fidelity Neural Network Computing
Residual Multi-Fidelity Neural Network ComputingBIT Numerical Mathematics (BIT), 2023
Owen Davis
Mohammad Motamed
Raúl Tempone
215
4
0
05 Oct 2023
Deep Ridgelet Transform: Voice with Koopman Operator Proves Universality
  of Formal Deep Networks
Deep Ridgelet Transform: Voice with Koopman Operator Proves Universality of Formal Deep Networks
Sho Sonoda
Yuka Hashimoto
Isao Ishikawa
Masahiro Ikeda
239
3
0
05 Oct 2023
Deep Networks as Denoising Algorithms: Sample-Efficient Learning of
  Diffusion Models in High-Dimensional Graphical Models
Deep Networks as Denoising Algorithms: Sample-Efficient Learning of Diffusion Models in High-Dimensional Graphical ModelsIEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2023
Song Mei
Yuchen Wu
DiffM
199
32
0
20 Sep 2023
The Boundaries of Verifiable Accuracy, Robustness, and Generalisation in
  Deep Learning
The Boundaries of Verifiable Accuracy, Robustness, and Generalisation in Deep LearningInternational Conference on Artificial Neural Networks (ICANN), 2023
Alexander Bastounis
Alexander N. Gorban
Anders C. Hansen
D. Higham
Danil Prokhorov
Oliver J. Sutton
I. Tyukin
Qinghua Zhou
OOD
148
4
0
13 Sep 2023
Approximation Results for Gradient Descent trained Neural Networks
Approximation Results for Gradient Descent trained Neural Networks
G. Welper
142
1
0
09 Sep 2023
Distribution learning via neural differential equations: a nonparametric
  statistical perspective
Distribution learning via neural differential equations: a nonparametric statistical perspectiveJournal of machine learning research (JMLR), 2023
Youssef Marzouk
Zhi Ren
Sven Wang
Jakob Zech
222
18
0
03 Sep 2023
CVFC: Attention-Based Cross-View Feature Consistency for Weakly
  Supervised Semantic Segmentation of Pathology Images
CVFC: Attention-Based Cross-View Feature Consistency for Weakly Supervised Semantic Segmentation of Pathology ImagesIEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2023
Liangrui Pan
Lian-min Wang
Zhichao Feng
Liwen Xu
Shaoliang Peng
190
3
0
21 Aug 2023
Capacity Bounds for Hyperbolic Neural Network Representations of Latent
  Tree Structures
Capacity Bounds for Hyperbolic Neural Network Representations of Latent Tree StructuresNeural Networks (Neural Netw.), 2023
Anastasis Kratsios
Rui Hong
Haitz Sáez de Ocáriz Borde
226
5
0
18 Aug 2023
Expressivity of Spiking Neural Networks
Expressivity of Spiking Neural Networks
Manjot Singh
Adalbert Fono
Gitta Kutyniok
296
3
0
16 Aug 2023
Classification of Data Generated by Gaussian Mixture Models Using Deep
  ReLU Networks
Classification of Data Generated by Gaussian Mixture Models Using Deep ReLU NetworksJournal of machine learning research (JMLR), 2023
Tiancong Zhou
X. Huo
167
5
0
15 Aug 2023
Fourier neural operator for learning solutions to macroscopic traffic
  flow models: Application to the forward and inverse problems
Fourier neural operator for learning solutions to macroscopic traffic flow models: Application to the forward and inverse problemsTransportation Research Part C: Emerging Technologies (TRC), 2023
Bilal Thonnam Thodi
Sai Venkata Ramana Ambadipudi
Saif Eddin Jabari
AI4CE
348
18
0
14 Aug 2023
On the Optimal Expressive Power of ReLU DNNs and Its Application in
  Approximation with Kolmogorov Superposition Theorem
On the Optimal Expressive Power of ReLU DNNs and Its Application in Approximation with Kolmogorov Superposition TheoremIEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023
Juncai He
176
15
0
10 Aug 2023
Optimal Approximation and Learning Rates for Deep Convolutional Neural
  Networks
Optimal Approximation and Learning Rates for Deep Convolutional Neural Networks
Shao-Bo Lin
163
1
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07 Aug 2023
Statistically Optimal Generative Modeling with Maximum Deviation from
  the Empirical Distribution
Statistically Optimal Generative Modeling with Maximum Deviation from the Empirical DistributionInternational Conference on Machine Learning (ICML), 2023
Elen Vardanyan
Sona Hunanyan
T. Galstyan
A. Minasyan
A. Dalalyan
330
2
0
31 Jul 2023
Deep Convolutional Neural Networks with Zero-Padding: Feature Extraction
  and Learning
Deep Convolutional Neural Networks with Zero-Padding: Feature Extraction and Learning
Zhixiong Han
Baichen Liu
Shao-Bo Lin
Ding-Xuan Zhou
156
6
0
30 Jul 2023
Weighted variation spaces and approximation by shallow ReLU networks
Weighted variation spaces and approximation by shallow ReLU networksApplied and Computational Harmonic Analysis (ACHA), 2023
Ronald A. DeVore
Robert D. Nowak
Rahul Parhi
Jonathan W. Siegel
247
6
0
28 Jul 2023
Rates of Approximation by ReLU Shallow Neural Networks
Rates of Approximation by ReLU Shallow Neural NetworksJournal of Complexity (J. Complexity), 2023
Tong Mao
Ding-Xuan Zhou
163
36
0
24 Jul 2023
Universal Approximation Theorem and error bounds for quantum neural networks and quantum reservoirs
Universal Approximation Theorem and error bounds for quantum neural networks and quantum reservoirsIEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2023
Lukas Gonon
A. Jacquier
304
21
0
24 Jul 2023
Deep Operator Network Approximation Rates for Lipschitz Operators
Deep Operator Network Approximation Rates for Lipschitz OperatorsAnalysis and Applications (AA), 2023
Ch. Schwab
A. Stein
Jakob Zech
177
18
0
19 Jul 2023
How Many Neurons Does it Take to Approximate the Maximum?
How Many Neurons Does it Take to Approximate the Maximum?ACM-SIAM Symposium on Discrete Algorithms (SODA), 2023
Itay Safran
Daniel Reichman
Paul Valiant
244
11
0
18 Jul 2023
Connections between Operator-splitting Methods and Deep Neural Networks
  with Applications in Image Segmentation
Connections between Operator-splitting Methods and Deep Neural Networks with Applications in Image SegmentationAnnals of Applied Mathematics (AAM), 2023
Hao Liu
X. Tai
Raymond H. F. Chan
274
3
0
18 Jul 2023
Deep Network Approximation: Beyond ReLU to Diverse Activation Functions
Deep Network Approximation: Beyond ReLU to Diverse Activation FunctionsJournal of machine learning research (JMLR), 2023
Shijun Zhang
Jianfeng Lu
Hongkai Zhao
367
49
0
13 Jul 2023
Polynomial Width is Sufficient for Set Representation with
  High-dimensional Features
Polynomial Width is Sufficient for Set Representation with High-dimensional FeaturesInternational Conference on Learning Representations (ICLR), 2023
Peihao Wang
Shenghao Yang
Shu Li
Zinan Lin
Pan Li
460
10
0
08 Jul 2023
Learning Theory of Distribution Regression with Neural Networks
Learning Theory of Distribution Regression with Neural NetworksConstructive approximation (Constr. Approx.), 2023
Zhongjie Shi
Zhan Yu
Ding-Xuan Zhou
171
5
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07 Jul 2023
Sumformer: Universal Approximation for Efficient Transformers
Sumformer: Universal Approximation for Efficient Transformers
Silas Alberti
Niclas Dern
L. Thesing
Gitta Kutyniok
257
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05 Jul 2023
Nonparametric Classification on Low Dimensional Manifolds using
  Overparameterized Convolutional Residual Networks
Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual NetworksNeural Information Processing Systems (NeurIPS), 2023
Kaiqi Zhang
Zixuan Zhang
Minshuo Chen
Yuma Takeda
Mengdi Wang
Tuo Zhao
Yu Wang
249
2
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04 Jul 2023
Neural Hilbert Ladders: Multi-Layer Neural Networks in Function Space
Neural Hilbert Ladders: Multi-Layer Neural Networks in Function SpaceJournal of machine learning research (JMLR), 2023
Zhengdao Chen
352
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03 Jul 2023
A Constructive Approach to Function Realization by Neural Stochastic
  Differential Equations
A Constructive Approach to Function Realization by Neural Stochastic Differential EquationsIEEE Conference on Decision and Control (CDC), 2023
Tanya Veeravalli
Maxim Raginsky
142
0
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01 Jul 2023
Effective Minkowski Dimension of Deep Nonparametric Regression: Function
  Approximation and Statistical Theories
Effective Minkowski Dimension of Deep Nonparametric Regression: Function Approximation and Statistical TheoriesInternational Conference on Machine Learning (ICML), 2023
Zixuan Zhang
Minshuo Chen
Mengdi Wang
Wenjing Liao
Tuo Zhao
227
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26 Jun 2023
On the Global Convergence of Natural Actor-Critic with Two-layer Neural
  Network Parametrization
On the Global Convergence of Natural Actor-Critic with Two-layer Neural Network Parametrization
Mudit Gaur
Amrit Singh Bedi
Di-di Wang
Vaneet Aggarwal
232
6
0
18 Jun 2023
Uniform Convergence of Deep Neural Networks with Lipschitz Continuous
  Activation Functions and Variable Widths
Uniform Convergence of Deep Neural Networks with Lipschitz Continuous Activation Functions and Variable WidthsIEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2023
Yuesheng Xu
Haizhang Zhang
275
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02 Jun 2023
Neural Differential Recurrent Neural Network with Adaptive Time Steps
Neural Differential Recurrent Neural Network with Adaptive Time Steps
Yixuan Tan
Liyan Xie
Xiuyuan Cheng
AI4TS
207
7
0
02 Jun 2023
Learning Prescriptive ReLU Networks
Learning Prescriptive ReLU NetworksInternational Conference on Machine Learning (ICML), 2023
Wei-Ju Sun
Asterios Tsiourvas
294
3
0
01 Jun 2023
Fine-grained analysis of non-parametric estimation for pairwise learning
Fine-grained analysis of non-parametric estimation for pairwise learning
Junyu Zhou
Shuo Huang
Han Feng
Puyu Wang
Ding-Xuan Zhou
348
1
0
31 May 2023
What and How does In-Context Learning Learn? Bayesian Model Averaging,
  Parameterization, and Generalization
What and How does In-Context Learning Learn? Bayesian Model Averaging, Parameterization, and GeneralizationInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Yufeng Zhang
Fengzhuo Zhang
Zhuoran Yang
Zhaoran Wang
BDL
357
93
0
30 May 2023
Masked Bayesian Neural Networks : Theoretical Guarantee and its
  Posterior Inference
Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior InferenceInternational Conference on Machine Learning (ICML), 2023
Insung Kong
Dongyoon Yang
Jongjin Lee
Ilsang Ohn
Gyuseung Baek
Yongdai Kim
BDL
236
8
0
24 May 2023
Statistical Guarantees of Group-Invariant GANs
Statistical Guarantees of Group-Invariant GANs
Ziyu Chen
Markos A. Katsoulakis
Luc Rey-Bellet
Wei-wei Zhu
608
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22 May 2023
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