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1908.11133
Cited By

On the rate of convergence of fully connected very deep neural network
regression estimates

v1v2v3v4v5 (latest)

On the rate of convergence of fully connected very deep neural network regression estimates

29 August 2019

ArXiv (abs)PDF HTML

Papers citing "On the rate of convergence of fully connected very deep neural network regression estimates"

25 / 25 papers shown

How Neural Networks Learn the Support is an Implicit Regularization
Effect of SGD

How Neural Networks Learn the Support is an Implicit Regularization Effect of SGD

Pierfrancesco Beneventano

Tomaso A. Poggio

364

4

0

17 Jun 2024

Soft Label PU Learning

Soft Label PU Learning

257

0

0

03 May 2024

Analysis of the expected $L_2$ error of an over-parametrized deep neural
network estimate learned by gradient descent without regularization

Analysis of the expected

L_2

error of an over-parametrized deep neural network estimate learned by gradient descent without regularization

271

4

0

24 Nov 2023

Intrinsic and extrinsic deep learning on manifolds

Intrinsic and extrinsic deep learning on manifoldsElectronic Journal of Statistics (EJS), 2023

201

5

0

16 Feb 2023

Semiparametric Regression for Spatial Data via Deep Learning

Semiparametric Regression for Spatial Data via Deep LearningSpatial Statistics (Spat. Stat.), 2023

345

11

0

10 Jan 2023

Precision Machine Learning

Precision Machine Learning

Eric J. Michaud

191

40

0

24 Oct 2022

Analysis of convolutional neural network image classifiers in a
rotationally symmetric model

Analysis of convolutional neural network image classifiers in a rotationally symmetric modelIEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2022

Benjamin Kohler

213

8

0

11 May 2022

A Deep Generative Approach to Conditional Sampling

A Deep Generative Approach to Conditional Sampling

Yuling Jiao

182

55

0

19 Oct 2021

Convergence rates of deep ReLU networks for multiclass classification

Convergence rates of deep ReLU networks for multiclass classification

Johannes Schmidt-Hieber

221

28

0

02 Aug 2021

Convergence rates for shallow neural networks learned by gradient
descent

Convergence rates for shallow neural networks learned by gradient descent

266

14

0

20 Jul 2021

Estimation of a regression function on a manifold by fully connected
deep neural networks

Estimation of a regression function on a manifold by fully connected deep neural networksJournal of Statistical Planning and Inference (JSPI), 2021

205

9

0

20 Jul 2021

Calibrating multi-dimensional complex ODE from noisy data via deep
neural networks

Calibrating multi-dimensional complex ODE from noisy data via deep neural networksJournal of Statistical Planning and Inference (JSPI), 2021

Fan Yang

215

7

0

07 Jun 2021

Analysis of convolutional neural network image classifiers in a
hierarchical max-pooling model with additional local pooling

Analysis of convolutional neural network image classifiers in a hierarchical max-pooling model with additional local pooling

Benjamin Walter

171

20

0

31 May 2021

A likelihood approach to nonparametric estimation of a singular
distribution using deep generative models

A likelihood approach to nonparametric estimation of a singular distribution using deep generative modelsJournal of machine learning research (JMLR), 2021

626

23

0

09 May 2021

Deep Nonparametric Regression on Approximate Manifolds: Non-Asymptotic
Error Bounds with Polynomial Prefactors

Deep Nonparametric Regression on Approximate Manifolds: Non-Asymptotic Error Bounds with Polynomial PrefactorsAnnals of Statistics (Ann. Stat.), 2021

Yuling Jiao

471

83

0

14 Apr 2021

Approximating smooth functions by deep neural networks with sigmoid
activation function

Approximating smooth functions by deep neural networks with sigmoid activation function

247

80

0

08 Oct 2020

A deep network construction that adapts to intrinsic dimensionality
beyond the domain

A deep network construction that adapts to intrinsic dimensionality beyond the domain

415

14

0

06 Aug 2020

Layer Sparsity in Neural Networks

Layer Sparsity in Neural Networks

Johannes Lederer

216

10

0

28 Jun 2020

Statistical Guarantees for Regularized Neural Networks

Statistical Guarantees for Regularized Neural NetworksNeural Networks (NN), 2020

Johannes Lederer

353

41

0

30 May 2020

On Deep Instrumental Variables Estimate

On Deep Instrumental Variables Estimate

258

27

0

30 Apr 2020

Nonconvex sparse regularization for deep neural networks and its
optimality

Nonconvex sparse regularization for deep neural networks and its optimalityNeural Computation (Neural Comput.), 2020

208

24

0

26 Mar 2020

On the rate of convergence of image classifiers based on convolutional
neural networks

On the rate of convergence of image classifiers based on convolutional neural networksAnnals of the Institute of Statistical Mathematics (AISM), 2020

Benjamin Walter

202

18

0

03 Mar 2020

Sharp Rate of Convergence for Deep Neural Network Classifiers under the
Teacher-Student Setting

Sharp Rate of Convergence for Deep Neural Network Classifiers under the Teacher-Student Setting

Tianyang Hu

317

19

0

19 Jan 2020

Over-parametrized deep neural networks do not generalize well

Over-parametrized deep neural networks do not generalize well

187

14

0

09 Dec 2019

Analysis of the rate of convergence of neural network regression
estimates which are easy to implement

Analysis of the rate of convergence of neural network regression estimates which are easy to implement

257

1

0

09 Dec 2019

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