12 citations · 37 across the 25 of their papers we have counts for
7 papers · 1 filter
CoSPlay: Cooperative Self-Play at Test-Time with Self-Generated Code and Unit Test
Zhangyi Hu, Chenhui Liu, Tian Huang +6
Recently, Reinforcement Learning with Verifiable Rewards (RLVR) and Test-Time Scaling (TTS) have advanced LLM code generation through executable verification. Yet Ground-Truth Unit…
AL-GNN: Privacy-Preserving and Replay-Free Continual Graph Learning via Analytic Learning
Xuling Zhang, Jindong Li, Yifei Zhang +2
Continual graph learning (CGL) aims to enable graph neural networks to incrementally learn from a stream of graph structured data without forgetting previously acquired knowledge.…
GLADMamba: Unsupervised Graph-Level Anomaly Detection Powered by Selective State Space Model
Yali Fu, Jindong Li, Qi Wang +1
Unsupervised graph-level anomaly detection (UGLAD) is a critical and challenging task across various domains, such as social network analysis, anti-cancer drug discovery, and toxic…
Robust and Efficient Writer-Independent IMU-Based Handwriting Recognition
Jindong Li, Tim Hamann, Jens Barth +3
Handwriting recognition (HWR) using inertial measurement unit (IMU) data remains challenging due to variations in writing styles and the limited availability of datasets. Previous…
HC-GLAD: Dual Hyperbolic Contrastive Learning for Unsupervised Graph-Level Anomaly Detection
Yali Fu, Jindong Li, Jiahong Liu +3
Unsupervised graph-level anomaly detection (UGAD) has garnered increasing attention in recent years due to its significance. Most existing methods that rely on traditional GNNs mai…
FANFOLD: Graph Normalizing Flows-driven Asymmetric Network for Unsupervised Graph-Level Anomaly Detection
Rui Cao, Shijie Xue, Jindong Li +2
Unsupervised graph-level anomaly detection (UGAD) has attracted increasing interest due to its widespread application. In recent studies, knowledge distillation-based methods have…
CVTGAD: Simplified Transformer with Cross-View Attention for Unsupervised Graph-level Anomaly Detection
Jindong Li, Qianli Xing, Qi Wang +1
Unsupervised graph-level anomaly detection (UGAD) has received remarkable performance in various critical disciplines, such as chemistry analysis and bioinformatics. Existing UGAD…