no code implementations • 1 Aug 2023 • Zhenyu Zhong, Qiliang Fan, Jiacheng Zhang, Minghua Ma, Shenglin Zhang, Yongqian Sun, QIngwei Lin, Yuzhi Zhang, Dan Pei
Internet-based services have seen remarkable success, generating vast amounts of monitored key performance indicators (KPIs) as univariate or multivariate time series.
no code implementations • 10 Mar 2022 • Junjie Shen, Ningfei Wang, Ziwen Wan, Yunpeng Luo, Takami Sato, Zhisheng Hu, Xinyang Zhang, Shengjian Guo, Zhenyu Zhong, Kang Li, Ziming Zhao, Chunming Qiao, Qi Alfred Chen
In this paper, we perform the first systematization of knowledge of such growing semantic AD AI security research space.
3 code implementations • 14 Sep 2021 • Ziyuan Zhong, Zhisheng Hu, Shengjian Guo, Xinyang Zhang, Zhenyu Zhong, Baishakhi Ray
We define the failures (e. g., car crashes) caused by the faulty MSF as fusion errors and develop a novel evolutionary-based domain-specific search framework, FusED, for the efficient detection of fusion errors.
no code implementations • 2 Jun 2021 • Zhisheng Hu, Shengjian Guo, Zhenyu Zhong, Kang Li
Simulation-based virtual testing has become an essential step to ensure the safety of autonomous driving systems.
no code implementations • 13 Mar 2021 • Zeyu Jiao, Huan Lei, Hengshan Zong, Yingjie Cai, Zhenyu Zhong
Escalator-related injuries threaten public health with the widespread use of escalators.
1 code implementation • 6 Mar 2020 • Bai Li, Shiqi Wang, Yunhan Jia, Yantao Lu, Zhenyu Zhong, Lawrence Carin, Suman Jana
Recent research has proposed the lottery ticket hypothesis, suggesting that for a deep neural network, there exist trainable sub-networks performing equally or better than the original model with commensurate training steps.
1 code implementation • ICLR 2020 • Yunhan Jia, Yantao Lu, Junjie Shen, Qi Alfred Chen, Hao Chen, Zhenyu Zhong, Tao Wei
Recent work in adversarial machine learning started to focus on the visual perception in autonomous driving and studied Adversarial Examples (AEs) for object detection models.
1 code implementation • 27 May 2019 • Yunhan Jia, Yantao Lu, Junjie Shen, Qi Alfred Chen, Zhenyu Zhong, Tao Wei
Recent work in adversarial machine learning started to focus on the visual perception in autonomous driving and studied Adversarial Examples (AEs) for object detection models.
1 code implementation • 8 May 2019 • Yunhan Jia, Yantao Lu, Senem Velipasalar, Zhenyu Zhong, Tao Wei
Neural networks are known to be vulnerable to carefully crafted adversarial examples, and these malicious samples often transfer, i. e., they maintain their effectiveness even against other models.