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A General Framework for quantifying Aleatoric and Epistemic uncertainty
  in Graph Neural Networks

A General Framework for quantifying Aleatoric and Epistemic uncertainty in Graph Neural Networks

20 May 2022
Sai Munikoti
D. Agarwal
Laya Das
Balasubramaniam Natarajan
    BDL
    UD
ArXivPDFHTML

Papers citing "A General Framework for quantifying Aleatoric and Epistemic uncertainty in Graph Neural Networks"

3 / 3 papers shown
Title
Uncertainty in Graph Neural Networks: A Survey
Uncertainty in Graph Neural Networks: A Survey
Fangxin Wang
Yuqing Liu
Kay Liu
Yibo Wang
Sourav Medya
Philip S. Yu
AI4CE
48
8
0
11 Mar 2024
Graph Convolutional Network with Connectivity Uncertainty for EEG-based
  Emotion Recognition
Graph Convolutional Network with Connectivity Uncertainty for EEG-based Emotion Recognition
Hongxiang Gao
Xiangyao Wang
Zhenghua Chen
Min-man Wu
Zhipeng Cai
Lulu Zhao
Jianqing Li
Chengyu Liu
30
9
0
22 Oct 2023
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
282
9,136
0
06 Jun 2015
1