3 citations · 6 across the 6 of their papers we have counts for
7 papers
MedTVL: Harnessing Vision and Language for Medical Time Series Classification
Jiexia Ye, Jia Li, Fugee Tsung
Recent advancements in multimodal learning for medical time series (MedTS) classification highlight the benefits of integrating complementary modalities for clinical decision. Howe…
Towards Anomaly-Aware Pre-Training and Fine-Tuning for Graph Anomaly Detection
Yunhui Liu, Jiashun Cheng, Yiqing Lin +7
Graph anomaly detection (GAD) has garnered increasing attention in recent years, yet remains challenging due to two key factors: (1) label scarcity stemming from the high cost of a…
MedSpaformer: a Transferable Transformer with Multi-granularity Token Sparsification for Medical Time Series Classification
Jiexia Ye, Weiqi Zhang, Ziyue Li +2
Accurate medical time series (MedTS) classification is essential for effective clinical diagnosis, yet remains challenging due to complex multi-channel temporal dependencies, infor…
Heterophilic Graph Neural Networks Optimization with Causal Message-passing
Botao Wang, Jia Li, Heng Chang +2
In this work, we discover that causal inference provides a promising approach to capture heterophilic message-passing in Graph Neural Network (GNN). By leveraging cause-effect anal…
MedualTime: A Dual-Adapter Language Model for Medical Time Series-Text Multimodal Learning
Jiexia Ye, Weiqi Zhang, Ziyue Li +3
The recent rapid advancements in language models (LMs) have garnered attention in medical time series-text multimodal learning. However, existing contrastive learning-based and pro…
Empowering Time Series Analysis with Foundation Models: A Comprehensive Survey
Jiexia Ye, Yongzi Yu, Weiqi Zhang +3
Time series data are ubiquitous across diverse real-world applications, making time series analysis critically important. Traditional approaches are largely task-specific, offering…
Robust Attributed Graph Alignment via Joint Structure Learning and Optimal Transport
Jianheng Tang, Weiqi Zhang, Jiajin Li +3
Graph alignment, which aims at identifying corresponding entities across multiple networks, has been widely applied in various domains. As the graphs to be aligned are usually cons…