Zobrazeno 1 - 10
of 370
pro vyhledávání: '"Yu, LuLu"'
Unbiased Learning to Rank (ULTR) aims to leverage biased implicit user feedback (e.g., click) to optimize an unbiased ranking model. The effectiveness of the existing ULTR methods has primarily been validated on synthetic datasets. However, their per
Externí odkaz:
http://arxiv.org/abs/2408.09817
Large language models (LLMs) have been found to produce hallucinations when the question exceeds their internal knowledge boundaries. A reliable model should have a clear perception of its knowledge boundaries, providing correct answers within its sc
Externí odkaz:
http://arxiv.org/abs/2408.09773
The Chinese academy of sciences Information Retrieval team (CIR) has participated in the NTCIR-17 ULTRE-2 task. This paper describes our approaches and reports our results on the ULTRE-2 task. We recognize the issue of false negatives in the Baidu se
Externí odkaz:
http://arxiv.org/abs/2310.11852
Autor:
Zhao, Chenyu, Ma, Minghua, Zhong, Zhenyu, Zhang, Shenglin, Tan, Zhiyuan, Xiong, Xiao, Yu, LuLu, Feng, Jiayi, Sun, Yongqian, Zhang, Yuzhi, Pei, Dan, Lin, Qingwei, Zhang, Dongmei
Proactive failure detection of instances is vitally essential to microservice systems because an instance failure can propagate to the whole system and degrade the system's performance. Over the years, many single-modal (i.e., metrics, logs, or trace
Externí odkaz:
http://arxiv.org/abs/2305.18985
An effective ranking model usually requires a large amount of training data to learn the relevance between documents and queries. User clicks are often used as training data since they can indicate relevance and are cheap to collect, but they contain
Externí odkaz:
http://arxiv.org/abs/2302.09340
Unbiased learning to rank (ULTR) aims to mitigate various biases existing in user clicks, such as position bias, trust bias, presentation bias, and learn an effective ranker. In this paper, we introduce our winning approach for the "Unbiased Learning
Externí odkaz:
http://arxiv.org/abs/2302.07530
Autor:
Dong, Xu1,2 (AUTHOR), Yu, Lulu3,4 (AUTHOR), Zhang, Qiang1,2 (AUTHOR), Yang, Ju1 (AUTHOR), Gong, Zhou1,2 (AUTHOR), Niu, Xiaogang4,5 (AUTHOR), Li, Hongwei4,5 (AUTHOR), Zhang, Xu1,2 (AUTHOR), Liu, Maili1,2 (AUTHOR), Jin, Changwen3,4,5 (AUTHOR) changwen@pku.edu.cn, Hu, Yunfei1,2 (AUTHOR) huyunfei@wipm.ac.cn
Publikováno v:
Communications Biology. 5/11/2024, Vol. 7 Issue 1, p1-11. 11p.
Autor:
Lu, Yan’e, Han, Lei, Wang, Xingxing, Liu, Xiaotong, Jia, Xinlei, Lan, Kunyi, Gao, Shumin, Feng, Zhendong, Yu, Lulu, Yang, Qian, Cui, Naixue, Wei, Ya Bin, Liu, Jia Jia
Publikováno v:
In Journal of Affective Disorders 1 December 2024 366:370-378
Publikováno v:
In International Journal of Pediatric Otorhinolaryngology July 2024 182
Publikováno v:
In The Journal of Minimally Invasive Gynecology July 2024 31(7):613-619