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Principles for Initialization and Architecture Selection in Graph Neural
  Networks with ReLU Activations

Principles for Initialization and Architecture Selection in Graph Neural Networks with ReLU Activations

20 June 2023
G. Dezoort
Boris Hanin
    AI4CE
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Papers citing "Principles for Initialization and Architecture Selection in Graph Neural Networks with ReLU Activations"

3 / 3 papers shown
Title
Quantitative CLTs in Deep Neural Networks
Quantitative CLTs in Deep Neural Networks
Stefano Favaro
Boris Hanin
Domenico Marinucci
I. Nourdin
G. Peccati
BDL
23
11
0
12 Jul 2023
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
M. Bronstein
Joan Bruna
Taco S. Cohen
Petar Velivcković
GNN
172
1,100
0
27 Apr 2021
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