Zobrazeno 1 - 10
of 413
pro vyhledávání: '"Lin, Yucheng"'
This study introduces a novel method for irony detection, applying Large Language Models (LLMs) with prompt-based learning to facilitate emotion-centric text augmentation. Traditional irony detection techniques typically fall short due to their relia
Externí odkaz:
http://arxiv.org/abs/2404.12291
Dense waveguide arrays with half-wavelength-pitch, low-crosstalk, broadband, and flexible routing capability are essential for integrated photonics. However, achieving such performance is challenging due to the relatively weaker confinement of dielec
Externí odkaz:
http://arxiv.org/abs/2211.14999
Autor:
Lin, Yucheng1 (AUTHOR), Zhang, Lu2 (AUTHOR), Shen, Sinuo1 (AUTHOR), Chen, Yuzhi1 (AUTHOR), Xu, Li1 (AUTHOR), Ji, Mingliang1 (AUTHOR), Guo, Yudong1 (AUTHOR), Wei, Jinan1 (AUTHOR), Li, Yonggang1 (AUTHOR), Wu, Xiaotao1 (AUTHOR), Lu, Jun1 (AUTHOR) lujun-joint@seu.edu.cn
Publikováno v:
Orthopaedic Surgery. Apr2024, Vol. 16 Issue 4, p902-911. 10p.
Autor:
Li, Pan, Hu, Qiuhui, Wang, Biao, Lin, Yucheng, Chen, Wei, Chang, Chun, Hu, Junhao, Pang, Shusheng
Publikováno v:
In Industrial Crops & Products 15 October 2024 218
Autor:
Chang, Qinpeng, Zheng, Tianyuan, Gao, Chenchen, Zheng, Xilai, Lin, Yucheng, Song, Xiaoran, Walther, Marc
Publikováno v:
In Journal of Environmental Management 27 February 2024 353
Autor:
Zhang, Huanhuan, Hou, Liutao, Zhang, Weihong, Lin, Yucheng, Liu, Xueli, Zhao, Shiqiang, Chang, Chun
Publikováno v:
In Bioresource Technology February 2024 394
Publikováno v:
In Fuel 1 January 2024 355
Graph neural networks have become an important tool for modeling structured data. In many real-world systems, intricate hidden information may exist, e.g., heterogeneity in nodes/edges, static node/edge attributes, and spatiotemporal node/edge featur
Externí odkaz:
http://arxiv.org/abs/2010.04554
In this paper, we focus on graph representation learning of heterogeneous information network (HIN), in which various types of vertices are connected by various types of relations. Most of the existing methods conducted on HIN revise homogeneous grap
Externí odkaz:
http://arxiv.org/abs/1912.10832
Deep neural networks have been widely used in text classification. However, it is hard to interpret the neural models due to the complicate mechanisms. In this work, we study the interpretability of a variant of the typical text classification model
Externí odkaz:
http://arxiv.org/abs/1910.11236