Zobrazeno 1 - 10
of 828
pro vyhledávání: '"WU, Siyuan"'
Autor:
Niu, Shutong, Wang, Ruoyu, Du, Jun, Yang, Gaobin, Tu, Yanhui, Wu, Siyuan, Qian, Shuangqing, Wu, Huaxin, Xu, Haitao, Zhang, Xueyang, Zhong, Guolong, Yu, Xindi, Chen, Jieru, Wang, Mengzhi, Cai, Di, Gao, Tian, Wan, Genshun, Ma, Feng, Pan, Jia, Gao, Jianqing
This technical report outlines our submission system for the CHiME-8 NOTSOFAR-1 Challenge. The primary difficulty of this challenge is the dataset recorded across various conference rooms, which captures real-world complexities such as high overlap r
Externí odkaz:
http://arxiv.org/abs/2409.02041
Autor:
Li, Zheng, Wu, Siyuan, Chen, Ruichuan, Aditya, Paarijaat, Akkus, Istemi Ekin, Vanga, Manohar, Zhang, Min, Li, Hao, Zhang, Yang
Machine learning (ML), driven by prominent paradigms such as centralized and federated learning, has made significant progress in various critical applications ranging from autonomous driving to face recognition. However, its remarkable success has b
Externí odkaz:
http://arxiv.org/abs/2408.02131
Autor:
Li, Hao, Li, Zheng, Wu, Siyuan, Hu, Chengrui, Ye, Yutong, Zhang, Min, Feng, Dengguo, Zhang, Yang
Most existing membership inference attacks (MIAs) utilize metrics (e.g., loss) calculated on the model's final state, while recent advanced attacks leverage metrics computed at various stages, including both intermediate and final stages, throughout
Externí odkaz:
http://arxiv.org/abs/2407.15098
Autor:
Wu, Siyuan, Huang, Yue, Gao, Chujie, Chen, Dongping, Zhang, Qihui, Wan, Yao, Zhou, Tianyi, Zhang, Xiangliang, Gao, Jianfeng, Xiao, Chaowei, Sun, Lichao
Large Language Models (LLMs) such as GPT-4 and Llama3 have significantly impacted various fields by enabling high-quality synthetic data generation and reducing dependence on expensive human-generated datasets. Despite this, challenges remain in the
Externí odkaz:
http://arxiv.org/abs/2406.18966
Large Language Models (LLMs) have garnered significant attention due to their remarkable ability to process information across various languages. Despite their capabilities, they exhibit inconsistencies in handling identical queries in different lang
Externí odkaz:
http://arxiv.org/abs/2406.14721
Autor:
Chen, Dongping, Huang, Yue, Wu, Siyuan, Tang, Jingyu, Chen, Liuyi, Bai, Yilin, He, Zhigang, Wang, Chenlong, Zhou, Huichi, Li, Yiqiang, Zhou, Tianshuo, Yu, Yue, Gao, Chujie, Zhang, Qihui, Gui, Yi, Li, Zhen, Wan, Yao, Zhou, Pan, Gao, Jianfeng, Sun, Lichao
Recently, Multimodal Large Language Models (MLLMs) have been used as agents to control keyboard and mouse inputs by directly perceiving the Graphical User Interface (GUI) and generating corresponding code. However, current agents primarily exhibit ex
Externí odkaz:
http://arxiv.org/abs/2406.10819
Autor:
Gao, Chujie, Zhang, Qihui, Chen, Dongping, Huang, Yue, Wu, Siyuan, Fu, Zhengyan, Wan, Yao, Zhang, Xiangliang, Sun, Lichao
Large Language Models (LLMs) have achieved remarkable success across various industries due to their exceptional generative capabilities. However, for safe and effective real-world deployments, ensuring honesty and helpfulness is critical. This paper
Externí odkaz:
http://arxiv.org/abs/2406.00380
Dynamic obstacle avoidance is a popular research topic for autonomous systems, such as micro aerial vehicles and service robots. Accurately evaluating the performance of dynamic obstacle avoidance methods necessitates the establishment of a metric to
Externí odkaz:
http://arxiv.org/abs/2404.14848
This paper proposes a decentralized trajectory planning framework for the collision avoidance problem of multiple micro aerial vehicles (MAVs) in environments with static and dynamic obstacles. The framework utilizes spatiotemporal occupancy grid map
Externí odkaz:
http://arxiv.org/abs/2404.15602
Autor:
Zhao, Wei, Hou, Zhitao, Wu, Siyuan, Gao, Yan, Dong, Haoyu, Wan, Yao, Zhang, Hongyu, Sui, Yulei, Zhang, Haidong
Writing formulas on spreadsheets, such as Microsoft Excel and Google Sheets, is a widespread practice among users performing data analysis. However, crafting formulas on spreadsheets remains a tedious and error-prone task for many end-users, particul
Externí odkaz:
http://arxiv.org/abs/2402.14853