Zobrazeno 1 - 10
of 769
pro vyhledávání: '"Zhu, YuXuan"'
Autor:
Huang, Yiming, Xiao, Jingyu, Tao, Lian, Zhang, Shuang-Nan, Yin, Qian-Qing, Wang, Yusa, Zhao, Zijian, Zhang, Chen, Zhao, Qingchang, Ma, Xiang, Zhao, Shujie, Zhou, Heng, Wen, Xiangyang, Li, Zhengwei, Xiong, Shaolin, Zhang, Juan, Bu, Qingcui, Cang, Jirong, Cao, Dezhi, Chen, Wen, Ding, Siran, Dai, Yanfeng, Gao, Min, Gao, Yang, He, Huilin, Hou, Shujin, Hou, Dongjie, Hu, Tai, Huang, Guoli, Huang, Yue, Jia, Liping, Jin, Ge, Li, Dalin, Li, Jinsong, Li, Panping, Li, Yajun, Liu, Xiaojing, Ma, Ruican, Men, Lingling, Pan, Xingyu, Qi, Liqiang, Song, Liming, Sun, Xianfei, Tang, Qingwen, Xiong, Liyuan, Xu, Yibo, Yang, Sheng, Yang, Yanji, Yang, Yong, Zhang, Aimei, Zhang, Wei, Zhang, Yifan, Zhang, Yueting, Zhao, Donghua, Zhao, Kang, Zhu, Yuxuan
The Chasing All Transients Constellation Hunters (CATCH) space mission is focused on exploring the dynamic universe via X-ray follow-up observations of various transients. The first pathfinder of the CATCH mission, CATCH-1, was launched on June 22, 2
Externí odkaz:
http://arxiv.org/abs/2410.17833
Autor:
Zhu, Yuxuan, Wang, Shiyi, Zhong, Wenqing, Shen, Nianchen, Li, Yunqi, Wang, Siqi, Li, Zhiheng, Wu, Cathy, He, Zhengbing, Li, Li
Artificial intelligence (AI) plays a crucial role in autonomous driving (AD) research, propelling its development towards intelligence and efficiency. Currently, the development of AD technology follows two main technical paths: modularization and en
Externí odkaz:
http://arxiv.org/abs/2409.14165
Graph Neural Networks (GNNs) are gaining popularity across various domains due to their effectiveness in learning graph-structured data. Nevertheless, they have been shown to be susceptible to backdoor poisoning attacks, which pose serious threats to
Externí odkaz:
http://arxiv.org/abs/2407.02431
Autor:
Ye, Wei, Xiao, Peng, Xu, Xiaofan, Zhu, Xiang, Yan, Yunbin, Wang, Lu, Ren, Jie, Zhu, Yuxuan, Xia, Ying, Rao, Xuan, Chang, Shoukang
In this work, we address the central problem about how to effectively find the available precision limit of unknown parameters. In the framework of the quantum Ziv-Zakai bound (QZZB), we employ noiseless linear amplification (NLA)techniques to an ini
Externí odkaz:
http://arxiv.org/abs/2404.14173
Publikováno v:
MLSys (2024)
Federated learning (FL) aims to train machine learning (ML) models across potentially millions of edge client devices. Yet, training and customizing models for FL clients is notoriously challenging due to the heterogeneity of client data, device capa
Externí odkaz:
http://arxiv.org/abs/2404.13515
Autor:
Chen, Xuexin, Cai, Ruichu, Huang, Zhengting, Zhu, Yuxuan, Horwood, Julien, Hao, Zhifeng, Li, Zijian, Hernandez-Lobato, Jose Miguel
We investigate the problem of explainability for machine learning models, focusing on Feature Attribution Methods (FAMs) that evaluate feature importance through perturbation tests. Despite their utility, FAMs struggle to distinguish the contribution
Externí odkaz:
http://arxiv.org/abs/2402.08845
Deep neural networks (DNNs) have been demonstrated to be vulnerable to well-crafted \emph{adversarial examples}, which are generated through either well-conceived $\mathcal{L}_p$-norm restricted or unrestricted attacks. Nevertheless, the majority of
Externí odkaz:
http://arxiv.org/abs/2312.13628
Autor:
Li, Xiaobo, Chen, Yong, Song, Liming, Cui, Weiwei, Li, Wei, Wang, Juan, Zhang, Shuang-Nan, Lu, Fangjun, Xu, Yupeng, Zhao, Haisheng, Ge, Mingyu, Tuo, Youli, Wang, Yusa, Chen, Tianxiang, Han, Dawei, Huo, Jia, Yang, Yanji, Li, Maoshun, Zhang, Ziliang, Zhu, Yuxuan, Zhao, Xiaofan
Purpose: The Low-Energy X-ray telescope (LE) is a main instrument of the Insight-HXMT mission and consists of 96 Swept Charge Devices (SCD) covering the 1-10 keV energy band. The energy gain and resolution are continuously calibrated by analysing Cas
Externí odkaz:
http://arxiv.org/abs/2302.10714
The explainability of Graph Neural Networks (GNNs) is critical to various GNN applications, yet it remains a significant challenge. A convincing explanation should be both necessary and sufficient simultaneously. However, existing GNN explaining appr
Externí odkaz:
http://arxiv.org/abs/2212.07056
Autor:
Zhu, Yuxuan1 (AUTHOR), Cheng, Yating1 (AUTHOR), Sun, Tao1,2 (AUTHOR), Wang, Ying3,4 (AUTHOR), Zhao, Guanlan5,6 (AUTHOR), Wang, Xiaohe1,2 (AUTHOR) xhewang@163.com, Wang, Feng1,2 (AUTHOR) wangfeng2022@hznu.edu.cn
Publikováno v:
BMC Public Health. 10/23/2024, Vol. 24 Issue 1, p1-12. 12p.
