Exploring Node Classification Uncertainty in Graph Neural Networks
Author:
Affiliation:
1. School of Data and Sciences, Brac University, Dhaka, Bangladesh
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3564746.3587019
Reference17 articles.
1. A review of uncertainty quantification in deep learning: Techniques, applications and challenges
2. The need for uncertainty quantification in machine-assisted medical decision making
3. Yarin Gal and Zoubin Ghahramani . 2016. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning (ICML'16). JMLR.org , New York, USA , 1050--1059. Yarin Gal and Zoubin Ghahramani. 2016. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning (ICML'16). JMLR.org, New York, USA, 1050--1059.
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