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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
Graph Neural Networks Exponentially Lose Expressive Power for Node
  Classification
Graph Neural Networks Exponentially Lose Expressive Power for Node Classification
Kenta Oono
Taiji Suzuki
GNN
329
30
0
27 May 2019
A Polynomial-Based Approach for Architectural Design and Learning with
  Deep Neural Networks
A Polynomial-Based Approach for Architectural Design and Learning with Deep Neural Networks
Joseph Daws
Clayton Webster
104
9
0
24 May 2019
How degenerate is the parametrization of neural networks with the ReLU
  activation function?
How degenerate is the parametrization of neural networks with the ReLU activation function?Neural Information Processing Systems (NeurIPS), 2019
Julius Berner
Dennis Elbrächter
Philipp Grohs
ODL
268
28
0
23 May 2019
On the minimax optimality and superiority of deep neural network
  learning over sparse parameter spaces
On the minimax optimality and superiority of deep neural network learning over sparse parameter spacesNeural Networks (NN), 2019
Satoshi Hayakawa
Taiji Suzuki
138
51
0
22 May 2019
Nonlinear Approximation and (Deep) ReLU Networks
Nonlinear Approximation and (Deep) ReLU NetworksConstructive approximation (Constr. Approx.), 2019
Ingrid Daubechies
Ronald A. DeVore
S. Foucart
Boris Hanin
G. Petrova
172
173
0
05 May 2019
Approximation spaces of deep neural networks
Approximation spaces of deep neural networksConstructive approximation (Constr. Approx.), 2019
Rémi Gribonval
Gitta Kutyniok
M. Nielsen
Felix Voigtländer
210
136
0
03 May 2019
A neural network-based framework for financial model calibration
A neural network-based framework for financial model calibration
Shuaiqiang Liu
Anastasia Borovykh
L. Grzelak
C. Oosterlee
157
114
0
23 Apr 2019
Depth Separations in Neural Networks: What is Actually Being Separated?
Depth Separations in Neural Networks: What is Actually Being Separated?
Itay Safran
Ronen Eldan
Ohad Shamir
MDE
230
39
0
15 Apr 2019
Approximation in $L^p(μ)$ with deep ReLU neural networks
Approximation in Lp(μ)L^p(μ)Lp(μ) with deep ReLU neural networks
F. Voigtlaender
P. Petersen
105
5
0
09 Apr 2019
Approximation Rates for Neural Networks with General Activation
  Functions
Approximation Rates for Neural Networks with General Activation Functions
Jonathan W. Siegel
Jinchao Xu
214
14
0
04 Apr 2019
Deep Neural Networks for Rotation-Invariance Approximation and Learning
Deep Neural Networks for Rotation-Invariance Approximation and Learning
C. Chui
Shao-Bo Lin
Ding-Xuan Zhou
233
36
0
03 Apr 2019
A Theoretical Analysis of Deep Neural Networks and Parametric PDEs
A Theoretical Analysis of Deep Neural Networks and Parametric PDEs
Gitta Kutyniok
P. Petersen
Mones Raslan
R. Schneider
288
215
0
31 Mar 2019
Approximation and Non-parametric Estimation of ResNet-type Convolutional
  Neural Networks
Approximation and Non-parametric Estimation of ResNet-type Convolutional Neural Networks
Kenta Oono
Taiji Suzuki
365
63
0
24 Mar 2019
Data Augmentation for Bayesian Deep Learning
Data Augmentation for Bayesian Deep Learning
YueXing Wang
Nicholas G. Polson
Vadim Sokolov
UQCVBDL
328
6
0
22 Mar 2019
Rectified deep neural networks overcome the curse of dimensionality for
  nonsmooth value functions in zero-sum games of nonlinear stiff systems
Rectified deep neural networks overcome the curse of dimensionality for nonsmooth value functions in zero-sum games of nonlinear stiff systems
C. Reisinger
Yufei Zhang
153
73
0
15 Mar 2019
Deep learning observables in computational fluid dynamics
Deep learning observables in computational fluid dynamics
K. Lye
Siddhartha Mishra
Deep Ray
OODAI4CE
334
169
0
07 Mar 2019
Theoretical guarantees for sampling and inference in generative models
  with latent diffusions
Theoretical guarantees for sampling and inference in generative models with latent diffusionsAnnual Conference Computational Learning Theory (COLT), 2019
Belinda Tzen
Maxim Raginsky
DiffM
253
115
0
05 Mar 2019
High-Dimensional Learning under ApproximateSparsity with Applications to
  Nonsmooth Estimation and Regularized Neural Networks
High-Dimensional Learning under ApproximateSparsity with Applications to Nonsmooth Estimation and Regularized Neural NetworksOperational Research (OR), 2019
Hongcheng Liu
Yinyu Ye
H. Lee
71
3
0
02 Mar 2019
Representation Learning with Weighted Inner Product for Universal
  Approximation of General Similarities
Representation Learning with Weighted Inner Product for Universal Approximation of General SimilaritiesInternational Joint Conference on Artificial Intelligence (IJCAI), 2019
Geewook Kim
Akifumi Okuno
Kazuki Fukui
Hidetoshi Shimodaira
176
9
0
27 Feb 2019
Nonlinear Approximation via Compositions
Nonlinear Approximation via CompositionsNeural Networks (NN), 2019
Zuowei Shen
Haizhao Yang
Shijun Zhang
408
98
0
26 Feb 2019
Error bounds for approximations with deep ReLU neural networks in
  $W^{s,p}$ norms
Error bounds for approximations with deep ReLU neural networks in Ws,pW^{s,p}Ws,p norms
Ingo Gühring
Gitta Kutyniok
P. Petersen
194
220
0
21 Feb 2019
Optimal Nonparametric Inference via Deep Neural Network
Optimal Nonparametric Inference via Deep Neural Network
Ruiqi Liu
B. Boukai
Zuofeng Shang
186
19
0
05 Feb 2019
Generalization Error Bounds of Gradient Descent for Learning
  Over-parameterized Deep ReLU Networks
Generalization Error Bounds of Gradient Descent for Learning Over-parameterized Deep ReLU Networks
Yuan Cao
Quanquan Gu
ODLMLTAI4CE
601
166
0
04 Feb 2019
Complexity of Linear Regions in Deep Networks
Complexity of Linear Regions in Deep Networks
Boris Hanin
David Rolnick
261
254
0
25 Jan 2019
When Can Neural Networks Learn Connected Decision Regions?
When Can Neural Networks Learn Connected Decision Regions?
Trung Le
Dinh Q. Phung
MLT
153
1
0
25 Jan 2019
Understanding Geometry of Encoder-Decoder CNNs
Understanding Geometry of Encoder-Decoder CNNs
J. C. Ye
Woon Kyoung Sung
3DVAI4CE
208
78
0
22 Jan 2019
The Oracle of DLphi
The Oracle of DLphi
Dominik Alfke
W. Baines
J. Blechschmidt
Mauricio J. del Razo Sarmina
Amnon Drory
...
L. Thesing
Philipp Trunschke
Johannes von Lindheim
David Weber
Melanie Weber
170
0
0
17 Jan 2019
Deep Neural Network Approximation Theory
Deep Neural Network Approximation Theory
Dennis Elbrächter
Dmytro Perekrestenko
Philipp Grohs
Helmut Bölcskei
341
243
0
08 Jan 2019
Realizing data features by deep nets
Realizing data features by deep nets
Zheng-Chu Guo
Lei Shi
Shao-Bo Lin
124
21
0
01 Jan 2019
Momen(e)t: Flavor the Moments in Learning to Classify Shapes
Momen(e)t: Flavor the Moments in Learning to Classify Shapes
Mor Joseph-Rivlin
Alon Zvirin
Ron Kimmel
3DPC
183
76
0
18 Dec 2018
Fast convergence rates of deep neural networks for classification
Fast convergence rates of deep neural networks for classification
Yongdai Kim
Ilsang Ohn
Dongha Kim
3DH3DV
204
85
0
10 Dec 2018
On variation of gradients of deep neural networks
On variation of gradients of deep neural networks
Yongdai Kim
Dongha Kim
ODLFAttMLT
94
0
0
02 Dec 2018
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU
  Networks
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks
Difan Zou
Yuan Cao
Dongruo Zhou
Quanquan Gu
ODL
542
452
0
21 Nov 2018
How Well Generative Adversarial Networks Learn Distributions
How Well Generative Adversarial Networks Learn DistributionsJournal of machine learning research (JMLR), 2018
Tengyuan Liang
GAN
325
110
0
07 Nov 2018
Size-Noise Tradeoffs in Generative Networks
Size-Noise Tradeoffs in Generative Networks
Bolton Bailey
Matus Telgarsky
125
21
0
26 Oct 2018
Adaptivity of deep ReLU network for learning in Besov and mixed smooth
  Besov spaces: optimal rate and curse of dimensionality
Adaptivity of deep ReLU network for learning in Besov and mixed smooth Besov spaces: optimal rate and curse of dimensionality
Taiji Suzuki
330
276
0
18 Oct 2018
Small ReLU networks are powerful memorizers: a tight analysis of
  memorization capacity
Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity
Chulhee Yun
S. Sra
Ali Jadbabaie
363
125
0
17 Oct 2018
Graph Embedding with Shifted Inner Product Similarity and Its Improved
  Approximation Capability
Graph Embedding with Shifted Inner Product Similarity and Its Improved Approximation Capability
Akifumi Okuno
Geewook Kim
Hidetoshi Shimodaira
97
8
0
04 Oct 2018
Understanding Weight Normalized Deep Neural Networks with Rectified
  Linear Units
Understanding Weight Normalized Deep Neural Networks with Rectified Linear Units
Yixi Xu
Tianlin Li
MQ
146
12
0
03 Oct 2018
Deep, Skinny Neural Networks are not Universal Approximators
Deep, Skinny Neural Networks are not Universal Approximators
Jesse Johnson
143
72
0
30 Sep 2018
Deep Neural Networks for Estimation and Inference
Deep Neural Networks for Estimation and Inference
M. Farrell
Tengyuan Liang
S. Misra
BDL
401
260
0
26 Sep 2018
A proof that deep artificial neural networks overcome the curse of
  dimensionality in the numerical approximation of Kolmogorov partial
  differential equations with constant diffusion and nonlinear drift
  coefficients
A proof that deep artificial neural networks overcome the curse of dimensionality in the numerical approximation of Kolmogorov partial differential equations with constant diffusion and nonlinear drift coefficients
Arnulf Jentzen
Diyora Salimova
Timo Welti
AI4CE
250
123
0
19 Sep 2018
Approximation and Estimation for High-Dimensional Deep Learning Networks
Approximation and Estimation for High-Dimensional Deep Learning Networks
Andrew R. Barron
Jason M. Klusowski
216
60
0
10 Sep 2018
Analysis of the Generalization Error: Empirical Risk Minimization over
  Deep Artificial Neural Networks Overcomes the Curse of Dimensionality in the
  Numerical Approximation of Black-Scholes Partial Differential Equations
Analysis of the Generalization Error: Empirical Risk Minimization over Deep Artificial Neural Networks Overcomes the Curse of Dimensionality in the Numerical Approximation of Black-Scholes Partial Differential Equations
Julius Berner
Philipp Grohs
Arnulf Jentzen
326
190
0
09 Sep 2018
A proof that artificial neural networks overcome the curse of
  dimensionality in the numerical approximation of Black-Scholes partial
  differential equations
A proof that artificial neural networks overcome the curse of dimensionality in the numerical approximation of Black-Scholes partial differential equations
Philipp Grohs
F. Hornung
Arnulf Jentzen
Philippe von Wurstemberger
341
185
0
07 Sep 2018
Equivalence of approximation by convolutional neural networks and
  fully-connected networks
Equivalence of approximation by convolutional neural networks and fully-connected networks
P. Petersen
Felix Voigtländer
240
83
0
04 Sep 2018
Deep Learning for Energy Markets
Deep Learning for Energy Markets
Michael Polson
Vadim Sokolov
AI4TS
193
27
0
16 Aug 2018
Collapse of Deep and Narrow Neural Nets
Collapse of Deep and Narrow Neural Nets
Lu Lu
Yanhui Su
George Karniadakis
ODL
244
164
0
15 Aug 2018
Application of Bounded Total Variation Denoising in Urban Traffic
  Analysis
Application of Bounded Total Variation Denoising in Urban Traffic Analysis
Shanshan Tang
Haijun Yu
47
1
0
04 Aug 2018
A machine learning framework for data driven acceleration of
  computations of differential equations
A machine learning framework for data driven acceleration of computations of differential equations
Siddhartha Mishra
AI4CE
179
92
0
25 Jul 2018
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