2 citations · 6 across the 5 of their papers we have counts for
5 papers
Fast Think-on-Graph: Wider, Deeper and Faster Reasoning of Large Language Model on Knowledge Graph
Xujian Liang, Zhaoquan Gu
Graph Retrieval Augmented Generation (GRAG) is a novel paradigm that takes the naive RAG system a step further by integrating graph information, such as knowledge graph (KGs), into…
FAT: Feature-Focusing Adversarial Training via Disentanglement of Natural and Perturbed Patterns
Yaguan Qian, Chenyu Zhao, Zhaoquan Gu +5
Deep neural networks (DNNs) are vulnerable to adversarial examples crafted by well-designed perturbations. This could lead to disastrous results on critical applications such as se…
When Less is Enough: Positive and Unlabeled Learning Model for Vulnerability Detection
Xin-Cheng Wen, Xinchen Wang, Cuiyun Gao +3
Automated code vulnerability detection has gained increasing attention in recent years. The deep learning (DL)-based methods, which implicitly learn vulnerable code patterns, have…
Adversarial Attacks on ASR Systems: An Overview
Xiao Zhang, Hao Tan, Xuan Huang +3
With the development of hardware and algorithms, ASR(Automatic Speech Recognition) systems evolve a lot. As The models get simpler, the difficulty of development and deployment bec…
Hessian-Free Second-Order Adversarial Examples for Adversarial Learning
Yaguan Qian, Yuqi Wang, Bin Wang +3
Recent studies show deep neural networks (DNNs) are extremely vulnerable to the elaborately designed adversarial examples. Adversarial learning with those adversarial examples has…