Zobrazeno 1 - 10
of 285
pro vyhledávání: '"Zhang, Borui"'
To incentivize flexible resources such as Battery Energy Storage Systems (BESSs) to offer Frequency Control Ancillary Services (FCAS), Australia's National Electricity Market (NEM) has implemented changes in recent years towards shorter-term bidding
Externí odkaz:
http://arxiv.org/abs/2406.00974
In this paper, we conduct a comprehensive study of the Next-to-Minimal Composite Higgs Model (NMCHM) extended with a dilaton field $\chi$ (denoted as NMCHM$_\chi$). A pseudo-Nambu-Goldstone boson (pNGB) $\eta$, resulting from the SO(6)$\to$SO(5) brea
Externí odkaz:
http://arxiv.org/abs/2404.05332
Rigorousness and clarity are both essential for interpretations of DNNs to engender human trust. Path methods are commonly employed to generate rigorous attributions that satisfy three axioms. However, the meaning of attributions remains ambiguous du
Externí odkaz:
http://arxiv.org/abs/2401.10442
3D occupancy prediction is an important task for the robustness of vision-centric autonomous driving, which aims to predict whether each point is occupied in the surrounding 3D space. Existing methods usually require 3D occupancy labels to produce me
Externí odkaz:
http://arxiv.org/abs/2311.12754
Shapley values have emerged as a widely accepted and trustworthy tool, grounded in theoretical axioms, for addressing challenges posed by black-box models like deep neural networks. However, computing Shapley values encounters exponential complexity
Externí odkaz:
http://arxiv.org/abs/2311.01010
Deep learning has revolutionized human society, yet the black-box nature of deep neural networks hinders further application to reliability-demanded industries. In the attempt to unpack them, many works observe or impact internal variables to improve
Externí odkaz:
http://arxiv.org/abs/2212.09062
Autor:
Zhang, Borui1 (AUTHOR), Chen, Kai1 (AUTHOR), Dai, Yelin1 (AUTHOR), Luo, Xi1 (AUTHOR), Xiong, Ziwei1 (AUTHOR), Zhang, Weijia1 (AUTHOR), Huang, Xiaodan1 (AUTHOR), So, Kwok-Fai1,2,3,4,5 (AUTHOR), Zhang, Li1,3,4,5 (AUTHOR) zhangli@jnu.edu.cn
Publikováno v:
Communications Biology. 10/1/2024, Vol. 7 Issue 1, p1-11. 11p.
Autor:
Zhang, Fan1 (AUTHOR), Zhang, Borui1 (AUTHOR), Cui, Tongshan1 (AUTHOR), Chen, Shanshan1 (AUTHOR), Zhang, Can1 (AUTHOR), Wang, Zhiwen1 (AUTHOR), Liu, Xili1,2 (AUTHOR) seedling@cau.edu.cn
Publikováno v:
PLoS Pathogens. 9/23/2024, Vol. 20 Issue 9, p1-29. 29p.
This paper proposes an attributable visual similarity learning (AVSL) framework for a more accurate and explainable similarity measure between images. Most existing similarity learning methods exacerbate the unexplainability by mapping each sample to
Externí odkaz:
http://arxiv.org/abs/2203.14932
Autor:
Yuan, Sha, Zhao, Hanyu, Zhao, Shuai, Leng, Jiahong, Liang, Yangxiao, Wang, Xiaozhi, Yu, Jifan, Lv, Xin, Shao, Zhou, He, Jiaao, Lin, Yankai, Han, Xu, Liu, Zhenghao, Ding, Ning, Rao, Yongming, Gao, Yizhao, Zhang, Liang, Ding, Ming, Fang, Cong, Wang, Yisen, Long, Mingsheng, Zhang, Jing, Dong, Yinpeng, Pang, Tianyu, Cui, Peng, Huang, Lingxiao, Liang, Zheng, Shen, Huawei, Zhang, Hui, Zhang, Quanshi, Dong, Qingxiu, Tan, Zhixing, Wang, Mingxuan, Wang, Shuo, Zhou, Long, Li, Haoran, Bao, Junwei, Pan, Yingwei, Zhang, Weinan, Yu, Zhou, Yan, Rui, Shi, Chence, Xu, Minghao, Zhang, Zuobai, Wang, Guoqiang, Pan, Xiang, Li, Mengjie, Chu, Xiaoyu, Yao, Zijun, Zhu, Fangwei, Cao, Shulin, Xue, Weicheng, Ma, Zixuan, Zhang, Zhengyan, Hu, Shengding, Qin, Yujia, Xiao, Chaojun, Zeng, Zheni, Cui, Ganqu, Chen, Weize, Zhao, Weilin, Yao, Yuan, Li, Peng, Zheng, Wenzhao, Zhao, Wenliang, Wang, Ziyi, Zhang, Borui, Fei, Nanyi, Hu, Anwen, Ling, Zenan, Li, Haoyang, Cao, Boxi, Han, Xianpei, Zhan, Weidong, Chang, Baobao, Sun, Hao, Deng, Jiawen, Zheng, Chujie, Li, Juanzi, Hou, Lei, Cao, Xigang, Zhai, Jidong, Liu, Zhiyuan, Sun, Maosong, Lu, Jiwen, Lu, Zhiwu, Jin, Qin, Song, Ruihua, Wen, Ji-Rong, Lin, Zhouchen, Wang, Liwei, Su, Hang, Zhu, Jun, Sui, Zhifang, Zhang, Jiajun, Liu, Yang, He, Xiaodong, Huang, Minlie, Tang, Jian, Tang, Jie
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm. Researchers have achieved various outcomes in the construction of BMs and the BM application in many fields. At present,
Externí odkaz:
http://arxiv.org/abs/2203.14101