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Analysis of Convolutions, Non-linearity and Depth in Graph Neural
  Networks using Neural Tangent Kernel

Analysis of Convolutions, Non-linearity and Depth in Graph Neural Networks using Neural Tangent Kernel

18 October 2022
Mahalakshmi Sabanayagam
P. Esser
D. Ghoshdastidar
ArXivPDFHTML

Papers citing "Analysis of Convolutions, Non-linearity and Depth in Graph Neural Networks using Neural Tangent Kernel"

2 / 2 papers shown
Title
Contextual Stochastic Block Models
Contextual Stochastic Block Models
Y. Deshpande
Andrea Montanari
Elchanan Mossel
S. Sen
98
151
0
23 Jul 2018
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train
  10,000-Layer Vanilla Convolutional Neural Networks
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks
Lechao Xiao
Yasaman Bahri
Jascha Narain Sohl-Dickstein
S. Schoenholz
Jeffrey Pennington
220
347
0
14 Jun 2018
1