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Universal approximation and model compression for radial neural networks

Universal approximation and model compression for radial neural networks

6 July 2021
I. Ganev
Twan van Laarhoven
Robin G. Walters
ArXivPDFHTML

Papers citing "Universal approximation and model compression for radial neural networks"

7 / 7 papers shown
Title
Symmetries, flat minima, and the conserved quantities of gradient flow
Symmetries, flat minima, and the conserved quantities of gradient flow
Bo-Lu Zhao
I. Ganev
Robin G. Walters
Rose Yu
Nima Dehmamy
42
16
0
31 Oct 2022
Symmetry Teleportation for Accelerated Optimization
Symmetry Teleportation for Accelerated Optimization
B. Zhao
Nima Dehmamy
Robin G. Walters
Rose Yu
ODL
15
20
0
21 May 2022
Vector Neurons: A General Framework for SO(3)-Equivariant Networks
Vector Neurons: A General Framework for SO(3)-Equivariant Networks
Congyue Deng
Or Litany
Yueqi Duan
A. Poulenard
Andrea Tagliasacchi
Leonidas J. Guibas
3DPC
102
314
0
25 Apr 2021
Size and Depth Separation in Approximating Benign Functions with Neural
  Networks
Size and Depth Separation in Approximating Benign Functions with Neural Networks
Gal Vardi
Daniel Reichman
T. Pitassi
Ohad Shamir
15
7
0
30 Jan 2021
What is the State of Neural Network Pruning?
What is the State of Neural Network Pruning?
Davis W. Blalock
Jose Javier Gonzalez Ortiz
Jonathan Frankle
John Guttag
178
1,027
0
06 Mar 2020
Universal Equivariant Multilayer Perceptrons
Universal Equivariant Multilayer Perceptrons
Siamak Ravanbakhsh
90
48
0
07 Feb 2020
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision
  Applications
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Andrew G. Howard
Menglong Zhu
Bo Chen
Dmitry Kalenichenko
Weijun Wang
Tobias Weyand
M. Andreetto
Hartwig Adam
3DH
948
20,471
0
17 Apr 2017
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