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1902.07896
Cited By
Error bounds for approximations with deep ReLU neural networks in
W
s
,
p
W^{s,p}
W
s
,
p
norms
21 February 2019
Ingo Gühring
Gitta Kutyniok
P. Petersen
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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
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41
0
0
12 Feb 2025
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
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
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
T. Getu
Georges Kaddoum
M. Bennis
40
1
0
13 Sep 2023
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
Yahong Yang
Haizhao Yang
Yang Xiang
31
19
0
15 May 2023
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
P. Jorgensen
Myung-Sin Song
James Tian
35
0
0
03 Jan 2023
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
1
d
1d
1
d
R. Gentile
G. Welper
ODL
52
6
0
17 Sep 2022
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
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
Tim De Ryck
Siddhartha Mishra
PINN
21
100
0
28 Jun 2021
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
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
N. R. Franco
Andrea Manzoni
P. Zunino
26
45
0
10 Mar 2021
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
Ben Adcock
Simone Brugiapaglia
N. Dexter
S. Moraga
36
29
0
11 Dec 2020
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
Ingo Gühring
Mones Raslan
Gitta Kutyniok
16
51
0
09 Jul 2020
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
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
Daria Fokina
Ivan V. Oseledets
21
18
0
28 Oct 2019
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
Gitta Kutyniok
P. Petersen
Mones Raslan
R. Schneider
20
197
0
31 Mar 2019
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
F. Malgouyres
22
7
0
31 May 2017
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