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Neural networks and rational functions

Neural networks and rational functions

11 June 2017
Matus Telgarsky
ArXivPDFHTML

Papers citing "Neural networks and rational functions"

15 / 15 papers shown
Title
Cauchy activation function and XNet
Cauchy activation function and XNet
Xin Li
Zhihong Xia
Hongkun Zhang
45
4
0
28 Sep 2024
rKAN: Rational Kolmogorov-Arnold Networks
rKAN: Rational Kolmogorov-Arnold Networks
Alireza Afzal Aghaei
44
18
0
20 Jun 2024
A comparison of rational and neural network based approximations
A comparison of rational and neural network based approximations
V. Peiris
R. D. Millán
N. Sukhorukova
J. Ugon
17
0
0
08 Mar 2023
Learning to Optimize for Reinforcement Learning
Learning to Optimize for Reinforcement Learning
Qingfeng Lan
Rupam Mahmood
Shuicheng Yan
Zhongwen Xu
OffRL
26
6
0
03 Feb 2023
Why Robust Generalization in Deep Learning is Difficult: Perspective of
  Expressive Power
Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power
Binghui Li
Jikai Jin
Han Zhong
J. Hopcroft
Liwei Wang
OOD
82
27
0
27 May 2022
On Regularizing Coordinate-MLPs
On Regularizing Coordinate-MLPs
Sameera Ramasinghe
L. MacDonald
Simon Lucey
156
5
0
01 Feb 2022
Activation Functions in Deep Learning: A Comprehensive Survey and
  Benchmark
Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark
S. Dubey
S. Singh
B. B. Chaudhuri
41
641
0
29 Sep 2021
Bridging the Gap between Spatial and Spectral Domains: A Unified
  Framework for Graph Neural Networks
Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks
Zhiqian Chen
Fanglan Chen
Lei Zhang
Taoran Ji
Kaiqun Fu
Liang Zhao
Feng Chen
Lingfei Wu
Charu C. Aggarwal
Chang-Tien Lu
41
18
0
21 Jul 2021
Parametric Complexity Bounds for Approximating PDEs with Neural Networks
Parametric Complexity Bounds for Approximating PDEs with Neural Networks
Tanya Marwah
Zachary Chase Lipton
Andrej Risteski
25
19
0
03 Mar 2021
Expressivity of Deep Neural Networks
Expressivity of Deep Neural Networks
Ingo Gühring
Mones Raslan
Gitta Kutyniok
16
51
0
09 Jul 2020
Padé Activation Units: End-to-end Learning of Flexible Activation
  Functions in Deep Networks
Padé Activation Units: End-to-end Learning of Flexible Activation Functions in Deep Networks
Alejandro Molina
P. Schramowski
Kristian Kersting
ODL
23
77
0
15 Jul 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
On representation power of neural network-based graph embedding and
  beyond
On representation power of neural network-based graph embedding and beyond
Akifumi Okuno
Hidetoshi Shimodaira
16
2
0
31 May 2018
Posterior Concentration for Sparse Deep Learning
Posterior Concentration for Sparse Deep Learning
Nicholas G. Polson
Veronika Rockova
UQCV
BDL
30
88
0
24 Mar 2018
Benefits of depth in neural networks
Benefits of depth in neural networks
Matus Telgarsky
148
602
0
14 Feb 2016
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