5 papers
A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges
Wei Ju, Siyu Yi, Yifan Wang +10
Graph-structured data exhibits universality and widespread applicability across diverse domains, such as social network analysis, biochemistry, financial fraud detection, and netwo…
Humanoid-inspired Causal Representation Learning for Domain Generalization
Ze Tao, Jian Zhang, Haowei Li +7
This paper proposes the Humanoid-inspired Structural Causal Model (HSCM), a novel causal framework inspired by human intelligence, designed to overcome the limitations of conventio…
C2F-TP: A Coarse-to-Fine Denoising Framework for Uncertainty-Aware Trajectory Prediction
Zichen Wang, Hao Miao, Senzhang Wang +3
Accurately predicting the trajectory of vehicles is critically important for ensuring safety and reliability in autonomous driving. Although considerable research efforts have been…
PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection
Ronghui Xu, Hao Miao, Senzhang Wang +2
With the proliferation of mobile sensing techniques, huge amounts of time series data are generated and accumulated in various domains, fueling plenty of real-world applications. I…
Score-CDM: Score-Weighted Convolutional Diffusion Model for Multivariate Time Series Imputation
S. Zhang, S. Wang, H. Miao +3
Multivariant time series (MTS) data are usually incomplete in real scenarios, and imputing the incomplete MTS is practically important to facilitate various time series mining task…