Graph Neural Network Introduction

A Review of Uncertainty Representation and Quantification in Neural Networks

Abstract: Effectively estimating the uncertainty attached to neural network predictions thus becomes essential to improve robustness, reliability, and trustworthiness. This paper provides an overview ...

IEEE

CLGSDN: Contrastive-Learning-Based Graph Structure Denoising Network for Traffic Prediction

Abstract: The graph neural network-based prediction models have demonstrated remarkable utility in traffic prediction, and their efficacy is highly determined by the quality of the provided graphs.

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A Review of Uncertainty Representation and Quantification in Neural Networks

CLGSDN: Contrastive-Learning-Based Graph Structure Denoising Network for Traffic Prediction

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