Deep evidential learning in diffusion convolutional recurrent neural network

Document Type

Article

Publication Date

1-1-2023

Abstract

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 Bayesian. But both methods need to sampling to gradually approximate the real distribution, in contrast, evidential deep learning formulates learning as an evidence acquisition process, which could get uncertainty quantification by placing evidential priors over the original Gaussian likelihood function and training the NN to infer the hyperparameters of the evidential distribution without sampling. So evidential deep learning (EDL) has its own advantage in measuring uncertainty. We apply it with diffusion convolutional recurrent neural network (DCRNN), and do the experiment in spatiotemporal forecasting task in a real-world traffic dataset. And we choose mean interval scores (MIS), a good metric for uncertainty quantification. We summarized the advantages of each method

Identifier

85150457257 (Scopus)

Publication Title

Electronic Research Archive

External Full Text Location

https://doi.org/10.3934/era.2023115

e-ISSN

26881594

First Page

2252

Last Page

2264

Issue

4

Volume

31

Grant

2153311

Fund Ref

National Science Foundation

This document is currently not available here.

Share

COinS