Zobrazeno 1 - 10
of 803
pro vyhledávání: '"WANG Maolin"'
Publikováno v:
Shuitu Baochi Xuebao, Vol 38, Iss 1, Pp 368-377 (2024)
[Objective] For the joint application of double-row high vertical nylon mesh sand barriers and grass checkerboard sand barriers, wind tunnel experimental studies were conducted to optimize the configuration of nylon mesh sand barriers porosity in the
Externí odkaz:
https://doaj.org/article/9fafb9fb5598472b9f5b8a2d4b3a8971
Autor:
Yuan Ye, Liu Jun, Jin Dou, Yue Zuogong, Yang Tao, Chen Ruijuan, Wang Maolin, Xu Lei, Hua Feng, Guo Yuqi, Tang Xiuchuan, He Xin, Yi Xinlei, Li Dong, Yu Wenwu, Zhang Hai-Tao, Chai Tianyou, Sui Shaochun, Ding Han
Publikováno v:
National Science Open, Vol 2 (2023)
Traditional machine learning relies on a centralized data pipeline for model training in various applications; however, data are inherently fragmented. Such a decentralized nature of databases presents the serious challenge for collaboration: sending
Externí odkaz:
https://doaj.org/article/33d9065847d348a683f503ee3bf9891a
Autor:
Liu, Ziwei, Liu, Qidong, Wang, Yejing, Wang, Wanyu, Jia, Pengyue, Wang, Maolin, Liu, Zitao, Chang, Yi, Zhao, Xiangyu
In various domains, Sequential Recommender Systems (SRS) have become essential due to their superior capability to discern intricate user preferences. Typically, SRS utilize transformer-based architectures to forecast the subsequent item within a seq
Externí odkaz:
http://arxiv.org/abs/2408.11451
Autor:
Yao, Jiacheng, Wang, Maolin, Chen, Wanqi, Jin, Chengxiang, Zhou, Jiajun, Yu, Shanqing, Xuan, Qi
The wide application of Ethereum technology has brought technological innovation to traditional industries. As one of Ethereum's core applications, smart contracts utilize diverse contract codes to meet various functional needs and have gained widesp
Externí odkaz:
http://arxiv.org/abs/2407.00336
In the rapidly evolving field of artificial intelligence, transformer-based models have gained significant attention in the context of Sequential Recommender Systems (SRSs), demonstrating remarkable proficiency in capturing user-item interactions. Ho
Externí odkaz:
http://arxiv.org/abs/2406.10244
Autor:
Luo, Sichun, Shao, Wei, Yao, Yuxuan, Xu, Jian, Liu, Mingyang, Li, Qintong, He, Bowei, Wang, Maolin, Deng, Guanzhi, Hou, Hanxu, Zhang, Xinyi, Song, Linqi
Nowadays, large language models (LLMs) have been integrated with conventional recommendation models to improve recommendation performance. However, while most of the existing works have focused on improving the model performance, the privacy issue ha
Externí odkaz:
http://arxiv.org/abs/2406.01363
Autor:
Zhang, Sheng, Wang, Maolin, Zhao, Yao, Zhuang, Chenyi, Gu, Jinjie, Guo, Ruocheng, Zhao, Xiangyu, Zhang, Zijian, Yin, Hongzhi
In this age where data is abundant, the ability to distill meaningful insights from the sea of information is essential. Our research addresses the computational and resource inefficiencies that current Sequential Recommender Systems (SRSs) suffer fr
Externí odkaz:
http://arxiv.org/abs/2402.00390
Autor:
Wang, Maolin, Pan, Yu, Xu, Zenglin, Guo, Ruocheng, Zhao, Xiangyu, Wang, Wanyu, Wang, Yiqi, Liu, Zitao, Liu, Langming
Temporal Point Processes (TPPs) hold a pivotal role in modeling event sequences across diverse domains, including social networking and e-commerce, and have significantly contributed to the advancement of recommendation systems and information retrie
Externí odkaz:
http://arxiv.org/abs/2402.00388
Autor:
Wang, Maolin, Zhao, Yao, Liu, Jiajia, Chen, Jingdong, Zhuang, Chenyi, Gu, Jinjie, Guo, Ruocheng, Zhao, Xiangyu
The deployment of Large Multimodal Models (LMMs) within AntGroup has significantly advanced multimodal tasks in payment, security, and advertising, notably enhancing advertisement audition tasks in Alipay. However, the deployment of such sizable mode
Externí odkaz:
http://arxiv.org/abs/2312.05795
Knowledge graphs (KGs), which consist of triples, are inherently incomplete and always require completion procedure to predict missing triples. In real-world scenarios, KGs are distributed across clients, complicating completion tasks due to privacy
Externí odkaz:
http://arxiv.org/abs/2311.10341