most citedNest-DGIL: Nesterov-optimized Deep Geometric Incremental Learning for CS Image Reconstruction

12 citations · 24 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL20242 cited

Leveraging Large Language Models for Solving Rare MIP Challenges

Teng Wang, Wing-Yin Yu, Ruifeng She +3

Mixed Integer Programming (MIP) has been extensively applied in areas requiring mathematical solvers to address complex instances within tight time constraints. However, as the pro…

cs.CV20232 cited

Backpropagation Path Search On Adversarial Transferability

Zhuoer Xu, Zhangxuan Gu, Jianping Zhang +3

Deep neural networks are vulnerable to adversarial examples, dictating the imperativeness to test the model's robustness before deployment. Transfer-based attackers craft adversari…

eess.IV202312 cited

Nest-DGIL: Nesterov-optimized Deep Geometric Incremental Learning for CS Image Reconstruction

Xiaohong Fan, Yin Yang, Ke Chen +2

Proximal gradient-based optimization is one of the most common strategies to solve inverse problem of images, and it is easy to implement. However, these techniques often generate…

cs.SE2023

Validating Multimedia Content Moderation Software via Semantic Fusion

Wenxuan Wang, Jingyuan Huang, Chang Chen +5

The exponential growth of social media platforms, such as Facebook and TikTok, has revolutionized communication and content publication in human society. Users on these platforms c…

cs.CV20231 cited

Improving the Transferability of Adversarial Samples by Path-Augmented Method

Jianping Zhang, Jen-tse Huang, Wenxuan Wang +5

Deep neural networks have achieved unprecedented success on diverse vision tasks. However, they are vulnerable to adversarial noise that is imperceptible to humans. This phenomenon…

cs.CV20236 cited

Transferable Adversarial Attacks on Vision Transformers with Token Gradient Regularization

Jianping Zhang, Yizhan Huang, Weibin Wu +1

Vision transformers (ViTs) have been successfully deployed in a variety of computer vision tasks, but they are still vulnerable to adversarial samples. Transfer-based attacks use a…