57 citations · 127 across the 8 of their papers we have counts for
8 papers
QA-LoRA: Quantization-Aware Low-Rank Adaptation of Large Language Models
Yuhui Xu, Lingxi Xie, Xiaotao Gu +6
Recently years have witnessed a rapid development of large language models (LLMs). Despite the strong ability in many language-understanding tasks, the heavy computational burden l…
Adversarially Robust Neural Architecture Search for Graph Neural Networks
Beini Xie, Heng Chang, Ziwei Zhang +5
Graph Neural Networks (GNNs) obtain tremendous success in modeling relational data. Still, they are prone to adversarial attacks, which are massive threats to applying GNNs to risk…
Knowledge Graph Completion with Counterfactual Augmentation
Heng Chang, Jie Cai, Jia Li
Graph Neural Networks (GNNs) have demonstrated great success in Knowledge Graph Completion (KGC) by modeling how entities and relations interact in recent years. However, most of t…
Revisiting Adversarial Attacks on Graph Neural Networks for Graph Classification
Xin Wang, Heng Chang, Beini Xie +5
Graph neural networks (GNNs) have achieved tremendous success in the task of graph classification and its diverse downstream real-world applications. Despite the huge success in le…
Semi-Supervised Hierarchical Graph Classification
Jia Li, Yongfeng Huang, Heng Chang +1
Node classification and graph classification are two graph learning problems that predict the class label of a node and the class label of a graph respectively. A node of a graph u…
Implicit Graph Neural Networks
Fangda Gu, Heng Chang, Wenwu Zhu +2
Graph Neural Networks (GNNs) are widely used deep learning models that learn meaningful representations from graph-structured data. Due to the finite nature of the underlying recur…
A Restricted Black-box Adversarial Framework Towards Attacking Graph Embedding Models
Heng Chang, Yu Rong, Tingyang Xu +5
With the great success of graph embedding model on both academic and industry area, the robustness of graph embedding against adversarial attack inevitably becomes a central proble…
Power up! Robust Graph Convolutional Network via Graph Powering
Ming Jin, Heng Chang, Wenwu Zhu +1
Graph convolutional networks (GCNs) are powerful tools for graph-structured data. However, they have been recently shown to be vulnerable to topological attacks. To enhance adversa…