Zobrazeno 1 - 10
of 307
pro vyhledávání: '"Fu Luoyi"'
Autor:
Zhao Yu, Wang Meng, Ding Jiaxin, Qi Jiexing, Wu Lyuwen, Zhang Sibo, Fu Luoyi, Wang Xinbing, Cheng Li
Publikováno v:
Journal of Data and Information Science, Vol 9, Iss 3, Pp 29-43 (2024)
This article presents an in-depth analysis of global research trends in Geosciences from 2014 to 2023. By integrating bibliometric analysis with expert insights from the Deeptime Digital Earth (DDE) initiative, this article identifies key emerging th
Externí odkaz:
https://doaj.org/article/78eedbea1af442f886249dd36bdbc842
Autor:
Ji, Huawei, Deng, Cheng, Xue, Bo, Jin, Zhouyang, Ding, Jiaxin, Gan, Xiaoying, Fu, Luoyi, Wang, Xinbing, Zhou, Chenghu
With the development of data-centric AI, the focus has shifted from model-driven approaches to improving data quality. Academic literature, as one of the crucial types, is predominantly stored in PDF formats and needs to be parsed into texts before f
Externí odkaz:
http://arxiv.org/abs/2409.10016
Autor:
Kang, Huquan, Fu, Luoyi, Funk, Russell J., Wang, Xinbing, Ding, Jiaxin, Liang, Shiyu, Wang, Jianghao, Zhou, Lei, Zhou, Chenghu
The past few centuries have witnessed a dramatic growth in scientific and technological knowledge. However, the nature of that growth - whether exponential or otherwise - remains controversial, perhaps partly due to the lack of quantitative character
Externí odkaz:
http://arxiv.org/abs/2409.08349
Autor:
Xu, Yi, Xue, Bo, Sheng, Shuqian, Deng, Cheng, Ding, Jiaxin, Shen, Zanwei, Fu, Luoyi, Wang, Xinbing, Zhou, Chenghu
In the ever-expanding landscape of academic research, the proliferation of ideas presents a significant challenge for researchers: discerning valuable ideas from the less impactful ones. The ability to efficiently evaluate the potential of these idea
Externí odkaz:
http://arxiv.org/abs/2409.13712
Autor:
Xue, Bo, Xu, Yi, Song, Yunchong, Pang, Yiming, Ren, Yuyang, Ding, Jiaxin, Fu, Luoyi, Wang, Xinbing
Traditional knowledge graph completion (KGC) methods rely solely on structural information, struggling with the inherent sparsity of knowledge graphs (KGs). Large Language Models (LLMs) learn extensive knowledge from large corpora with powerful conte
Externí odkaz:
http://arxiv.org/abs/2408.06787
The explosive growth of data fuels data-driven research, facilitating progress across diverse domains. The FAIR principles emerge as a guiding standard, aiming to enhance the findability, accessibility, interoperability, and reusability of data. Howe
Externí odkaz:
http://arxiv.org/abs/2408.04673
In Constrained Reinforcement Learning (CRL), agents explore the environment to learn the optimal policy while satisfying constraints. The penalty function method has recently been studied as an effective approach for handling constraints, which impos
Externí odkaz:
http://arxiv.org/abs/2407.15537
Autor:
Lu, Bin, Zhao, Ze, Han, Luyu, Gan, Xiaoying, Zhou, Yuntao, Zhou, Lei, Fu, Luoyi, Wang, Xinbing, Zhou, Chenghu, Zhang, Jing
Accurately reconstructing the global ocean deoxygenation over a century is crucial for assessing and protecting marine ecosystem. Existing expert-dominated numerical simulations fail to catch up with the dynamic variation caused by global warming and
Externí odkaz:
http://arxiv.org/abs/2405.07233
Autor:
Sheng, Shuqian, Xu, Yi, Zhang, Tianhang, Shen, Zanwei, Fu, Luoyi, Ding, Jiaxin, Zhou, Lei, Gan, Xiaoying, Wang, Xinbing, Zhou, Chenghu
The era of Large Language Models (LLMs) raises new demands for automatic evaluation metrics, which should be adaptable to various application scenarios while maintaining low cost and effectiveness. Traditional metrics for automatic text evaluation ar
Externí odkaz:
http://arxiv.org/abs/2404.19563
Graph neural networks (GNN) have achieved remarkable success in a wide range of tasks by encoding features combined with topology to create effective representations. However, the fundamental problem of understanding and analyzing how graph topology
Externí odkaz:
http://arxiv.org/abs/2404.07493