Graph Posterior Network: Bayesian Predictive Uncertainty for Node
Classification

Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification

26 October 2021

Maximilian Stadler

Bertrand Charpentier

Stephan Günnemann

Papers citing "Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification"

7 / 57 papers shown

Title
Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges S. Thais P. Calafiura G. Chachamis G. Dezoort Javier Mauricio Duarte S. Ganguly Michael Kagan D. Murnane Mark S. Neubauer K. Terao PINN AI4CE 17 30 0 23 Mar 2022
Prior and Posterior Networks: A Survey on Evidential Deep Learning Methods For Uncertainty Estimation Dennis Ulmer Christian Hardmeier J. Frellsen BDL UQCV UD EDL PER 29 47 0 06 Oct 2021
Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions Xuefeng Du Tian Bian Yu Rong Bo Han Tongliang Liu Tingyang Xu Wenbing Huang Yixuan Li Junzhou Huang NoLa 14 11 0 14 Jun 2021
Natural Posterior Network: Deep Bayesian Uncertainty for Exponential Family Distributions Bertrand Charpentier Oliver Borchert Daniel Zügner Simon Geisler Stephan Günnemann UQCV BDL 14 17 0 10 May 2021
Uncertainty Aware Semi-Supervised Learning on Graph Data Xujiang Zhao Feng Chen Shu Hu Jin-Hee Cho UQCV EDL BDL 102 130 0 24 Oct 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 268 5,635 0 05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 247 9,042 0 06 Jun 2015