Zobrazeno 1 - 10
of 429
pro vyhledávání: '"Wang Yihang"'
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
In recent years, the investment amount of university engineering construction has been increasing significantly, and the establishment of scientific and reasonable cost management system has become particularly important in the investment control of
Externí odkaz:
https://doaj.org/article/ae610ac8d1db4d12a12244e70b9365d9
Autor:
Chen Sisi, Deng Jieru, Cheng Peidong, Zhang Zhaolong, Wang Yihang, Chen Shuwen, Tang Yan, Wang Tianyu, Yang Guiyan
Publikováno v:
BMC Genomics, Vol 23, Iss 1, Pp 1-17 (2022)
Abstract Walnut is an important economic tree species while confronting with global environmental stress, resulting in decline in quality and yield. Therefore, it is urgent to elucidate the molecular mechanism for the regulation of walnut response to
Externí odkaz:
https://doaj.org/article/978635d903a9498783253bc6d0aaa41c
Autor:
Wu, Xingjian, Qiu, Xiangfei, Li, Zhengyu, Wang, Yihang, Hu, Jilin, Guo, Chenjuan, Xiong, Hui, Yang, Bin
Anomaly detection in multivariate time series is challenging as heterogeneous subsequence anomalies may occur. Reconstruction-based methods, which focus on learning nomral patterns in the frequency domain to detect diverse abnormal subsequences, achi
Externí odkaz:
http://arxiv.org/abs/2410.12261
Autor:
Li, Zhe, Qiu, Xiangfei, Chen, Peng, Wang, Yihang, Cheng, Hanyin, Shu, Yang, Hu, Jilin, Guo, Chenjuan, Zhou, Aoying, Wen, Qingsong, Jensen, Christian S., Yang, Bin
Time Series Forecasting (TSF) is key functionality in numerous fields, including in finance, weather services, and energy management. While TSF methods are emerging these days, many of them require domain-specific data collection and model training a
Externí odkaz:
http://arxiv.org/abs/2410.11802
Generative LLM have achieved significant success in various industrial tasks and can effectively adapt to vertical domains and downstream tasks through ICL. However, with tasks becoming increasingly complex, the context length required by ICL is also
Externí odkaz:
http://arxiv.org/abs/2408.10497
Although instruction tuning is widely used to adjust behavior in Large Language Models (LLMs), extensive empirical evidence and research indicates that it is primarily a process where the model fits to specific task formats, rather than acquiring new
Externí odkaz:
http://arxiv.org/abs/2408.10841
In-context learning (ICL) capabilities are foundational to the success of large language models (LLMs). Recently, context compression has attracted growing interest since it can largely reduce reasoning complexities and computation costs of LLMs. In
Externí odkaz:
http://arxiv.org/abs/2408.00274
Autor:
Wu, Yizhang, Li, Yuan, Liu, Yihan, Zhu, Dashuai, Xing, Sicheng, Lambert, Noah, Weisbecker, Hannah, Liu, Siyuan, Davis, Brayden, Zhang, Lin, Wang, Meixiang, Yuan, Gongkai, You, Chris Zhoufan, Zhang, Anran, Duncan, Cate, Xie, Wanrong, Wang, Yihang, Wang, Yong, Kanamurlapudi, Sreya, Evert, Garcia-Guzman, Putcha, Arjun, Dickey, Michael D., Huang, Ke, Bai, Wubin
Bioelectronic implants with soft mechanics, biocompatibility, and excellent electrical performance enable biomedical implants to record electrophysiological signals and execute interventions within internal organs, promising to revolutionize the diag
Externí odkaz:
http://arxiv.org/abs/2406.13956
Autor:
Wang, Yihang, Qiu, Yuying, Chen, Peng, Zhao, Kai, Shu, Yang, Rao, Zhongwen, Pan, Lujia, Yang, Bin, Guo, Chenjuan
With the increasing collection of time series data from various domains, there arises a strong demand for general time series forecasting models pre-trained on a large number of time-series datasets to support a variety of downstream prediction tasks
Externí odkaz:
http://arxiv.org/abs/2405.17478
Large Language Models (LLMs) have excelled in various tasks but perform better in high-resource scenarios, which presents challenges in low-resource scenarios. Data scarcity and the inherent difficulty of adapting LLMs to specific tasks compound the
Externí odkaz:
http://arxiv.org/abs/2404.00914