Zobrazeno 1 - 10
of 103
pro vyhledávání: '"Mo, LinJian"'
Autor:
Wu, Kaixin, Ji, Yixin, Chen, Zeyuan, Wang, Qiang, Wang, Cunxiang, Liu, Hong, Ji, Baijun, Xu, Jia, Liu, Zhongyi, Gu, Jinjie, Zhou, Yuan, Mo, Linjian
Relevance modeling between queries and items stands as a pivotal component in commercial search engines, directly affecting the user experience. Given the remarkable achievements of large language models (LLMs) in various natural language processing
Externí odkaz:
http://arxiv.org/abs/2412.01269
Autor:
Geng, Binzong, Huan, Zhaoxin, Zhang, Xiaolu, He, Yong, Zhang, Liang, Yuan, Fajie, Zhou, Jun, Mo, Linjian
With the rise of large language models (LLMs), recent works have leveraged LLMs to improve the performance of click-through rate (CTR) prediction. However, we argue that a critical obstacle remains in deploying LLMs for practical use: the efficiency
Externí odkaz:
http://arxiv.org/abs/2403.19347
Autor:
Huan, Zhaoxin, Ding, Ke, Li, Ang, Zhang, Xiaolu, Min, Xu, He, Yong, Zhang, Liang, Zhou, Jun, Mo, Linjian, Gu, Jinjie, Liu, Zhongyi, Zhong, Wenliang, Zhang, Guannan
Click-through rate (CTR) prediction is a crucial issue in recommendation systems. There has been an emergence of various public CTR datasets. However, existing datasets primarily suffer from the following limitations. Firstly, users generally click d
Externí odkaz:
http://arxiv.org/abs/2308.16437
Autor:
Dong, Xin, Wu, Ruize, Xiong, Chao, Li, Hai, Cheng, Lei, He, Yong, Qian, Shiyou, Cao, Jian, Mo, Linjian
Publikováno v:
Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 2022: 386-395
Multi-task learning (MTL) aims at solving multiple related tasks simultaneously and has experienced rapid growth in recent years. However, MTL models often suffer from performance degeneration with negative transfer due to learning several tasks simu
Externí odkaz:
http://arxiv.org/abs/2301.13465
Click-Through Rate (CTR) prediction plays an important role in many industrial applications, and recently a lot of attention is paid to the deep interest models which use attention mechanism to capture user interests from historical behaviors. Howeve
Externí odkaz:
http://arxiv.org/abs/2104.06312
Akademický článek
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Autor:
Chen, Yang, Zhang, Peng, Liao, Jinling, Cheng, Jiwen, Zhang, Qin, Li, Tianyu, Zhang, Haiying, Jiang, Yonghua, Zhang, Fangxing, Zeng, Yanyu, Mo, Linjian, Yan, Haibiao, Liu, Deyun, Zhang, Qinyun, Zou, Chunlin, Wei, Gong-Hong, Mo, Zengnan
Publikováno v:
In Journal of Genetics and Genomics November 2022 49(11):1002-1015
Akademický článek
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Akademický článek
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Autor:
Liao, NaiKai, Tan, ShuTing, Yang, ShuBo, Zhai, GaoQiang, Li, ChengYang, Li, TianYu, Chen, Yang, Mo, LinJian, Cheng, JiWen
Publikováno v:
International braz j urol, Volume: 49, Issue: 2, Pages: 194-201, Published: 09 JUN 2023
Objectives To compare the dusting efficiency and safety with basketing for treating renal stones ≤ 2 cm during flexible ureteroscopy (fURS). Materials and methods This study included 218 patients with renal stones ≤ 2 cm treated with fURS. Among
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______608::9483879085343f78363f4d34bb2937eb
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1677-55382023000200194&lng=en&tlng=en
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1677-55382023000200194&lng=en&tlng=en