25 citations · 38 across the 6 of their papers we have counts for
6 papers
LLM-based Privacy Data Augmentation Guided by Knowledge Distillation with a Distribution Tutor for Medical Text Classification
Yiping Song, Juhua Zhang, Zhiliang Tian +3
As sufficient data are not always publically accessible for model training, researchers exploit limited data with advanced learning algorithms or expand the dataset via data augmen…
Normality Learning-based Graph Anomaly Detection via Multi-Scale Contrastive Learning
Jingcan Duan, Pei Zhang, Siwei Wang +5
Graph anomaly detection (GAD) has attracted increasing attention in machine learning and data mining. Recent works have mainly focused on how to capture richer information to impro…
One-step Multi-view Clustering with Diverse Representation
Xinhang Wan, Jiyuan Liu, Xinwang Liu +5
Multi-view clustering has attracted broad attention due to its capacity to utilize consistent and complementary information among views. Although tremendous progress has been made…
GuardFL: Safeguarding Federated Learning Against Backdoor Attacks through Attributed Client Graph Clustering
Hao Yu, Chuan Ma, Meng Liu +5
Federated Learning (FL) offers collaborative model training without data sharing but is vulnerable to backdoor attacks, where poisoned model weights lead to compromised system inte…
Deep Temporal Graph Clustering
Meng Liu, Yue Liu, Ke Liang +4
Deep graph clustering has recently received significant attention due to its ability to enhance the representation learning capabilities of models in unsupervised scenarios. Nevert…
GraphLearner: Graph Node Clustering with Fully Learnable Augmentation
Xihong Yang, Erxue Min, Ke Liang +6
Contrastive deep graph clustering (CDGC) leverages the power of contrastive learning to group nodes into different clusters. The quality of contrastive samples is crucial for achie…