activity
20162024
most citedTITAN: A Spatiotemporal Feature Learning Framework for Traffic Incident Duration Prediction

20 citations · 58 across the 18 of their papers we have counts for

collaborators

25 papers

cs.CL20249 cited

Exploring the Deceptive Power of LLM-Generated Fake News: A Study of Real-World Detection Challenges

Yanshen Sun, Jianfeng He, Limeng Cui +2

Recent advancements in Large Language Models (LLMs) have enabled the creation of fake news, particularly in complex fields like healthcare. Studies highlight the gap in the decepti…

cs.LG20234 cited

ALERTA-Net: A Temporal Distance-Aware Recurrent Networks for Stock Movement and Volatility Prediction

Shengkun Wang, YangXiao Bai, Kaiqun Fu +3

For both investors and policymakers, forecasting the stock market is essential as it serves as an indicator of economic well-being. To this end, we harness the power of social medi…

cs.RO2023

Learning Decentralized Flocking Controllers with Spatio-Temporal Graph Neural Network

Siji Chen, Yanshen Sun, Peihan Li +2

Recently a line of researches has delved the use of graph neural networks (GNNs) for decentralized control in swarm robotics. However, it has been observed that relying solely on t…

cs.CV2023

Self-Correlation and Cross-Correlation Learning for Few-Shot Remote Sensing Image Semantic Segmentation

Linhan Wang, Shuo Lei, Jianfeng He +3

Remote sensing image semantic segmentation is an important problem for remote sensing image interpretation. Although remarkable progress has been achieved, existing deep neural net…

cs.CL2023

TART: Improved Few-shot Text Classification Using Task-Adaptive Reference Transformation

Shuo Lei, Xuchao Zhang, Jianfeng He +2

Meta-learning has emerged as a trending technique to tackle few-shot text classification and achieve state-of-the-art performance. However, the performance of existing approaches h…

cs.LG20234 cited

DG-Trans: Dual-level Graph Transformer for Spatiotemporal Incident Impact Prediction on Traffic Networks

Yanshen Sun, Kaiqun Fu, Chang-Tien Lu

The prompt estimation of traffic incident impacts can guide commuters in their trip planning and improve the resilience of transportation agencies' decision-making on resilience. H…