Deep evidential learning in diffusion convolutional recurrent neural network

Deep evidential learning in diffusion convolutional recurrent neural network

Graph neural networks (GNNs) is applied successfully in many graph tasks, but there still exists a limitation that many of GNNs model do not consider uncertainty quantification of its output predictions. For uncertainty quantification, there are mainly two types of methods which are frequentist and...

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Bibliographic Details
Main Authors: Zhiyuan Feng, Kai Qi, Bin Shi, Hao Mei, Qinghua Zheng, Hua Wei
Format: Article
Language:English
Published: AIMS Press 2023-02-01
Series:Electronic Research Archive
Subjects:
uncertainty quantification
evidential deep learning
graph neural network
spatiotemporal forecasting
regression task
Online Access:https://www.aimspress.com/article/doi/10.3934/era.2023115?viewType=HTML

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https://www.aimspress.com/article/doi/10.3934/era.2023115?viewType=HTML

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