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20222025
most citedMinimalist Traffic Prediction: Linear Layer Is All You Need

4 citations · 7 across the 5 of their papers we have counts for

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5 papers · 1 filter

cs.LG2025

Dynamic Localisation of Spatial-Temporal Graph Neural Network

Wenying Duan, Shujun Guo, Wei huang +2

Spatial-temporal data, fundamental to many intelligent applications, reveals dependencies indicating causal links between present measurements at specific locations and historical…

cs.LG2024

Pre-Training Identification of Graph Winning Tickets in Adaptive Spatial-Temporal Graph Neural Networks

Wenying Duan, Tianxiang Fang, Hong Rao +1

In this paper, we present a novel method to significantly enhance the computational efficiency of Adaptive Spatial-Temporal Graph Neural Networks (ASTGNNs) by introducing the conce…

cs.LG2023★ 4 cited

Minimalist Traffic Prediction: Linear Layer Is All You Need

Wenying Duan, Hong Rao, Wei Huang +1

Traffic prediction is essential for the progression of Intelligent Transportation Systems (ITS) and the vision of smart cities. While Spatial-Temporal Graph Neural Networks (STGNNs…

cs.LG2023★ 2 cited

Localised Adaptive Spatial-Temporal Graph Neural Network

Wenying Duan, Xiaoxi He, Zimu Zhou +2

Spatial-temporal graph models are prevailing for abstracting and modelling spatial and temporal dependencies. In this work, we ask the following question: whether and to what exten…

cs.LG2022★ 1 cited

Combating Distribution Shift for Accurate Time Series Forecasting via Hypernetworks

Wenying Duan, Xiaoxi He, Lu Zhou +2

Time series forecasting has widespread applications in urban life ranging from air quality monitoring to traffic analysis. However, accurate time series forecasting is challenging…