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RAN-GNNs: breaking the capacity limits of graph neural networks

RAN-GNNs: breaking the capacity limits of graph neural networks

29 March 2021
D. Valsesia
Giulia Fracastoro
E. Magli
    GNN
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Papers citing "RAN-GNNs: breaking the capacity limits of graph neural networks"

6 / 6 papers shown
Title
Multimodal Data Integration for Oncology in the Era of Deep Neural
  Networks: A Review
Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review
Asim Waqas
Aakash Tripathi
Ravichandran Ramachandran
Paul Stewart
Ghulam Rasool
AI4CE
37
31
0
11 Mar 2023
Graph Convolutional Neural Networks with Diverse Negative Samples via
  Decomposed Determinant Point Processes
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point Processes
Wei Duan
Junyu Xuan
Maoying Qiao
Jie Lu
28
8
0
05 Dec 2022
Benchmarking Graph Neural Networks
Benchmarking Graph Neural Networks
Vijay Prakash Dwivedi
Chaitanya K. Joshi
Anh Tuan Luu
T. Laurent
Yoshua Bengio
Xavier Bresson
189
916
0
02 Mar 2020
Representation Learning on Graphs with Jumping Knowledge Networks
Representation Learning on Graphs with Jumping Knowledge Networks
Keyulu Xu
Chengtao Li
Yonglong Tian
Tomohiro Sonobe
Ken-ichi Kawarabayashi
Stefanie Jegelka
GNN
267
1,945
0
09 Jun 2018
Geometric deep learning: going beyond Euclidean data
Geometric deep learning: going beyond Euclidean data
M. Bronstein
Joan Bruna
Yann LeCun
Arthur Szlam
P. Vandergheynst
GNN
253
3,239
0
24 Nov 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
285
9,138
0
06 Jun 2015
1