Jindong Li
Orcid: 0009-0007-2228-3696Affiliations:
- HKUST, Hong Kong, SAR, China
- Jilin University, Changchu, China
According to our database1,
Jindong Li authored at least 18 papers
between 2023 and 2026.
Collaborative distances:
Collaborative distances:
Timeline
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Bibliography
2026
IEEE Trans. Pattern Anal. Mach. Intell., May, 2026
HC-GLAD: Dual hyperbolic contrastive learning for unsupervised graph-level anomaly detection.
Neural Networks, 2026
Data-efficient CLIP-powered dual-branch networks for source-free unsupervised domain adaptation.
Expert Syst. Appl., 2026
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2026
Beyond Retraining: Training-Free Unknown Class Filtering for Source-Free Open Set Domain Adaptation of Vision-Language Models.
Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026
2025
AL-GNN: Privacy-Preserving and Replay-Free Continual Graph Learning via Analytic Learning.
CoRR, December, 2025
HyperbolicRAG: Enhancing Retrieval-Augmented Generation with Hyperbolic Representations.
CoRR, November, 2025
CoRR, September, 2025
Revisiting CLIP for SF-OSDA: Unleashing Zero-Shot Potential with Adaptive Threshold and Training-Free Feature Filtering.
CoRR, April, 2025
Cogito, ergo sum: A Neurobiologically-Inspired Cognition-Memory-Growth System for Code Generation.
CoRR, January, 2025
LCD-Net: A Lightweight Remote Sensing Change Detection Network Combining Feature Fusion and Gating Mechanism.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2025
GLADMamba: Unsupervised Graph-Level Anomaly Detection Powered by Selective State Space Model.
Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2025
2024
CoRR, 2024
FANFOLD: Graph Normalizing Flows-driven Asymmetric Network for Unsupervised Graph-Level Anomaly Detection.
CoRR, 2024
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024
2023
CVTGAD: Simplified Transformer with Cross-View Attention for Unsupervised Graph-Level Anomaly Detection.
Proceedings of the Machine Learning and Knowledge Discovery in Databases: Research Track, 2023