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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

21 February 2019
Ingo Gühring
Gitta Kutyniok
P. Petersen
ArXivPDFHTML

Papers citing "Error bounds for approximations with deep ReLU neural networks in $W^{s,p}$ norms"

28 / 28 papers shown
Title
Learning Discontinuous Galerkin Solutions to Elliptic Problems via Small Linear Convolutional Neural Networks
Learning Discontinuous Galerkin Solutions to Elliptic Problems via Small Linear Convolutional Neural Networks
A. Celaya
Yimo Wang
David T. Fuentes
Beatrice Riviere
41
0
0
12 Feb 2025
On the optimal approximation of Sobolev and Besov functions using deep
  ReLU neural networks
On the optimal approximation of Sobolev and Besov functions using deep ReLU neural networks
Yunfei Yang
62
2
0
02 Sep 2024
Leveraging Interpolation Models and Error Bounds for Verifiable Scientific Machine Learning
Leveraging Interpolation Models and Error Bounds for Verifiable Scientific Machine Learning
Tyler Chang
Andrew Gillette
R. Maulik
49
2
0
04 Apr 2024
Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of
  Experts
Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts
Anastasis Kratsios
Haitz Sáez de Ocáriz Borde
Takashi Furuya
Marc T. Law
MoE
41
1
0
05 Feb 2024
Fundamental Limits of Deep Learning-Based Binary Classifiers Trained with Hinge Loss
Fundamental Limits of Deep Learning-Based Binary Classifiers Trained with Hinge Loss
T. Getu
Georges Kaddoum
M. Bennis
40
1
0
13 Sep 2023
Rates of Approximation by ReLU Shallow Neural Networks
Rates of Approximation by ReLU Shallow Neural Networks
Tong Mao
Ding-Xuan Zhou
29
19
0
24 Jul 2023
Nearly Optimal VC-Dimension and Pseudo-Dimension Bounds for Deep Neural
  Network Derivatives
Nearly Optimal VC-Dimension and Pseudo-Dimension Bounds for Deep Neural Network Derivatives
Yahong Yang
Haizhao Yang
Yang Xiang
31
19
0
15 May 2023
Deep neural network approximation of composite functions without the
  curse of dimensionality
Deep neural network approximation of composite functions without the curse of dimensionality
Adrian Riekert
24
0
0
12 Apr 2023
Operator theory, kernels, and Feedforward Neural Networks
Operator theory, kernels, and Feedforward Neural Networks
P. Jorgensen
Myung-Sin Song
James Tian
35
0
0
03 Jan 2023
Limitations of neural network training due to numerical instability of
  backpropagation
Limitations of neural network training due to numerical instability of backpropagation
Clemens Karner
V. Kazeev
P. Petersen
32
3
0
03 Oct 2022
Approximation results for Gradient Descent trained Shallow Neural
  Networks in $1d$
Approximation results for Gradient Descent trained Shallow Neural Networks in 1d1d1d
R. Gentile
G. Welper
ODL
52
6
0
17 Sep 2022
Variational Physics Informed Neural Networks: the role of quadratures
  and test functions
Variational Physics Informed Neural Networks: the role of quadratures and test functions
S. Berrone
C. Canuto
Moreno Pintore
23
40
0
05 Sep 2021
Inverse-Dirichlet Weighting Enables Reliable Training of Physics
  Informed Neural Networks
Inverse-Dirichlet Weighting Enables Reliable Training of Physics Informed Neural Networks
S. Maddu
D. Sturm
Christian L. Müller
I. Sbalzarini
AI4CE
26
81
0
02 Jul 2021
Error analysis for physics informed neural networks (PINNs)
  approximating Kolmogorov PDEs
Error analysis for physics informed neural networks (PINNs) approximating Kolmogorov PDEs
Tim De Ryck
Siddhartha Mishra
PINN
21
100
0
28 Jun 2021
ReLU Deep Neural Networks from the Hierarchical Basis Perspective
ReLU Deep Neural Networks from the Hierarchical Basis Perspective
Juncai He
Lin Li
Jinchao Xu
AI4CE
28
30
0
10 May 2021
On the approximation of functions by tanh neural networks
On the approximation of functions by tanh neural networks
Tim De Ryck
S. Lanthaler
Siddhartha Mishra
21
137
0
18 Apr 2021
A Deep Learning approach to Reduced Order Modelling of Parameter
  Dependent Partial Differential Equations
A Deep Learning approach to Reduced Order Modelling of Parameter Dependent Partial Differential Equations
N. R. Franco
Andrea Manzoni
P. Zunino
26
45
0
10 Mar 2021
Friedrichs Learning: Weak Solutions of Partial Differential Equations
  via Deep Learning
Friedrichs Learning: Weak Solutions of Partial Differential Equations via Deep Learning
Fan Chen
J. Huang
Chunmei Wang
Haizhao Yang
28
30
0
15 Dec 2020
Deep Neural Networks Are Effective At Learning High-Dimensional
  Hilbert-Valued Functions From Limited Data
Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data
Ben Adcock
Simone Brugiapaglia
N. Dexter
S. Moraga
36
29
0
11 Dec 2020
Neural Network Approximation: Three Hidden Layers Are Enough
Neural Network Approximation: Three Hidden Layers Are Enough
Zuowei Shen
Haizhao Yang
Shijun Zhang
30
115
0
25 Oct 2020
Expressivity of Deep Neural Networks
Expressivity of Deep Neural Networks
Ingo Gühring
Mones Raslan
Gitta Kutyniok
16
51
0
09 Jul 2020
Deep Network Approximation for Smooth Functions
Deep Network Approximation for Smooth Functions
Jianfeng Lu
Zuowei Shen
Haizhao Yang
Shijun Zhang
67
247
0
09 Jan 2020
Deep Learning via Dynamical Systems: An Approximation Perspective
Deep Learning via Dynamical Systems: An Approximation Perspective
Qianxiao Li
Ting Lin
Zuowei Shen
AI4TS
AI4CE
19
107
0
22 Dec 2019
Growing axons: greedy learning of neural networks with application to
  function approximation
Growing axons: greedy learning of neural networks with application to function approximation
Daria Fokina
Ivan V. Oseledets
21
18
0
28 Oct 2019
Universal Approximation with Deep Narrow Networks
Universal Approximation with Deep Narrow Networks
Patrick Kidger
Terry Lyons
31
324
0
21 May 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
20
197
0
31 Mar 2019
Deep Neural Network Approximation Theory
Deep Neural Network Approximation Theory
Dennis Elbrächter
Dmytro Perekrestenko
Philipp Grohs
Helmut Bölcskei
14
207
0
08 Jan 2019
On the stable recovery of deep structured linear networks under sparsity
  constraints
On the stable recovery of deep structured linear networks under sparsity constraints
F. Malgouyres
22
7
0
31 May 2017
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