252 citations · 276 across the 6 of their papers we have counts for
6 papers
Data Pricing for Graph Neural Networks without Pre-purchased Inspection
Yiping Liu, Mengxiao Zhang, Jiamou Liu +1
Machine learning (ML) models have become essential tools in various scenarios. Their effectiveness, however, hinges on a substantial volume of data for satisfactory performance. Mo…
Spatial-Temporal Conditional Random Field for Human Trajectory Prediction
Pengqian Han, Jiamou Liu, Jialing He +3
Trajectory prediction is of significant importance in computer vision. Accurate pedestrian trajectory prediction benefits autonomous vehicles and robots in planning their motion. P…
GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendation
Song Yang, Jiamou Liu, Kaiqi Zhao
Next POI recommendation intends to forecast users' immediate future movements given their current status and historical information, yielding great values for both users and servic…
USER: Unsupervised Structural Entropy-based Robust Graph Neural Network
Yifei Wang, Yupan Wang, Zeyu Zhang +3
Unsupervised/self-supervised graph neural networks (GNN) are vulnerable to inherent randomness in the input graph data which greatly affects the performance of the model in downstr…
GACAN: Graph Attention-Convolution-Attention Networks for Traffic Forecasting Based on Multi-granularity Time Series
Sikai Zhang, Hong Zheng, Hongyi Su +3
Traffic forecasting is an integral part of intelligent transportation systems (ITS). Achieving a high prediction accuracy is a challenging task due to a high level of dynamics and…
Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting
Song Yang, Jiamou Liu, Kaiqi Zhao
Traffic flow forecasting is a crucial task in urban computing. The challenge arises as traffic flows often exhibit intrinsic and latent spatio-temporal correlations that cannot be…