230 citations · 376 across the 34 of their papers we have counts for
7 papers · 1 filter
Hybrid Learning with New Value Function for the Maximum Common Subgraph Problem
Yanli Liu, Jiming Zhao, Chu-Min Li +2
Maximum Common induced Subgraph (MCS) is an important NP-hard problem with wide real-world applications. Branch-and-Bound (BnB) is the basis of a class of efficient algorithms for…
Sampling-based Fast Gradient Rescaling Method for Highly Transferable Adversarial Attacks
Xu Han, Anmin Liu, Yifeng Xiong +2
Deep neural networks have shown to be very vulnerable to adversarial examples crafted by adding human-imperceptible perturbations to benign inputs. After achieving impressive attac…
Robust Textual Embedding against Word-level Adversarial Attacks
Yichen Yang, Xiaosen Wang, Kun He
We attribute the vulnerability of natural language processing models to the fact that similar inputs are converted to dissimilar representations in the embedding space, leading to…
Effective Variable Depth Local Search for the Budgeted Maximum Coverage Problem
Jianrong Zhou, Jiongzhi Zheng, Kun He
We address the Budgeted Maximum Coverage Problem (BMCP), which is a natural and more practical extension of the standard 0-1 knapsack problem and the set cover problem. Given m ele…
An Efficient Algorithm for the Partitioning Min-Max Weighted Matching Problem
Yuxuan Wang, Jinyao Xie, Jiongzhi Zheng +1
The Partitioning Min-Max Weighted Matching (PMMWM) problem is an NP-hard problem that combines the problem of partitioning a group of vertices of a bipartite graph into disjoint su…
A Strengthened Branch and Bound Algorithm for the Maximum Common (Connected) Subgraph Problem
Jianrong Zhou, Kun He, Jiongzhi Zheng +2
We propose a new and strengthened Branch-and-Bound (BnB) algorithm for the maximum common (connected) induced subgraph problem based on two new operators, Long-Short Memory (LSM) a…
TextHacker: Learning based Hybrid Local Search Algorithm for Text Hard-label Adversarial Attack
Zhen Yu, Xiaosen Wang, Wanxiang Che +1
Existing textual adversarial attacks usually utilize the gradient or prediction confidence to generate adversarial examples, making it hard to be deployed in real-world application…