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2206.04360
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A general approximation lower bound in
L
p
L^p
L
p
norm, with applications to feed-forward neural networks
9 June 2022
El Mehdi Achour
Armand Foucault
Sébastien Gerchinovitz
Franccois Malgouyres
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Papers citing
"A general approximation lower bound in $L^p$ norm, with applications to feed-forward neural networks"
14 / 14 papers shown
Title
Optimal learning of high-dimensional classification problems using deep neural networks
P. Petersen
F. Voigtlaender
60
10
0
23 Dec 2021
VC dimension of partially quantized neural networks in the overparametrized regime
Yutong Wang
Clayton D. Scott
76
1
0
06 Oct 2021
Optimal Approximation Rate of ReLU Networks in terms of Width and Depth
Zuowei Shen
Haizhao Yang
Shijun Zhang
157
118
0
28 Feb 2021
Sharp Bounds on the Approximation Rates, Metric Entropy, and
n
n
n
-widths of Shallow Neural Networks
Jonathan W. Siegel
Jinchao Xu
123
87
0
29 Jan 2021
The phase diagram of approximation rates for deep neural networks
Dmitry Yarotsky
Anton Zhevnerchuk
62
121
0
22 Jun 2019
Universal Approximation with Deep Narrow Networks
Patrick Kidger
Terry Lyons
130
333
0
21 May 2019
Optimal approximation of continuous functions by very deep ReLU networks
Dmitry Yarotsky
192
293
0
10 Feb 2018
Approximation beats concentration? An approximation view on inference with smooth radial kernels
M. Belkin
95
69
0
10 Jan 2018
Optimal approximation of piecewise smooth functions using deep ReLU neural networks
P. Petersen
Felix Voigtländer
223
475
0
15 Sep 2017
Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks
Peter L. Bartlett
Nick Harvey
Christopher Liaw
Abbas Mehrabian
208
432
0
08 Mar 2017
Error bounds for approximations with deep ReLU networks
Dmitry Yarotsky
195
1,230
0
03 Oct 2016
L p -norm Sauer-Shelah Lemma for Margin Multi-category Classifiers
Y. Guermeur
61
22
0
26 Sep 2016
Benefits of depth in neural networks
Matus Telgarsky
367
609
0
14 Feb 2016
Covering Numbers for Convex Functions
Adityanand Guntuboyina
B. Sen
89
77
0
31 Mar 2012
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