Zobrazeno 1 - 10
of 939
pro vyhledávání: '"Zhou Chenghu"'
Autor:
Ye, Nanyang, Sun, Qiao, Wang, Yifei, Yang, Liujia, Zhou, Jundong, Wang, Lei, Yang, Guang-Zhong, Wang, Xinbing, Zhou, Chenghu, Ren, Wei, Gu, Leilei, Wu, Huaqiang, Gu, Qinying
Analog computing using non-volatile memristors has emerged as a promising solution for energy-efficient deep learning. New materials, like perovskites-based memristors are recently attractive due to their cost-effectiveness, energy efficiency and fle
Externí odkaz:
http://arxiv.org/abs/2412.02779
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
Missing values are prevalent in multivariate time series, compromising the integrity of analyses and degrading the performance of downstream tasks. Consequently, research has focused on multivariate time series imputation, aiming to accurately impute
Externí odkaz:
http://arxiv.org/abs/2408.05740
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:
Zhou, Jianping, Lu, Bin, Liu, Zhanyu, Pan, Siyu, Feng, Xuejun, Wei, Hua, Zheng, Guanjie, Wang, Xinbing, Zhou, Chenghu
Due to detector malfunctions and communication failures, missing data is ubiquitous during the collection of traffic data. Therefore, it is of vital importance to impute the missing values to facilitate data analysis and decision-making for Intellige
Externí odkaz:
http://arxiv.org/abs/2406.03511
Recent vision-language pre-trained models (VL-PTMs) have shown remarkable success in open-vocabulary tasks. However, downstream use cases often involve further fine-tuning of VL-PTMs, which may distort their general knowledge and impair their ability
Externí odkaz:
http://arxiv.org/abs/2405.16417
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