Zobrazeno 1 - 10
of 2 391
pro vyhledávání: '"TING YAO"'
Autor:
Echterhoff, Jessica, Faghri, Fartash, Vemulapalli, Raviteja, Hu, Ting-Yao, Li, Chun-Liang, Tuzel, Oncel, Pouransari, Hadi
Large Language Models (LLMs) are frequently updated due to data or architecture changes to improve their performance. When updating models, developers often focus on increasing overall performance metrics with less emphasis on being compatible with p
Externí odkaz:
http://arxiv.org/abs/2407.09435
Autor:
Hsu, Ting-Yao, Huang, Chieh-Yang, Huang, Shih-Hong, Rossi, Ryan, Kim, Sungchul, Yu, Tong, Giles, C. Lee, Huang, Ting-Hao K.
Crafting effective captions for figures is important. Readers heavily depend on these captions to grasp the figure's message. However, despite a well-developed set of AI technologies for figures and captions, these have rarely been tested for usefuln
Externí odkaz:
http://arxiv.org/abs/2403.17784
Autor:
Hsu, Ting-Yao, Huang, Chieh-Yang, Rossi, Ryan, Kim, Sungchul, Giles, C. Lee, Huang, Ting-Hao K.
There is growing interest in systems that generate captions for scientific figures. However, assessing these systems output poses a significant challenge. Human evaluation requires academic expertise and is costly, while automatic evaluation depends
Externí odkaz:
http://arxiv.org/abs/2310.15405
Autor:
Su, Hsuan, Hu, Ting-Yao, Koppula, Hema Swetha, Vemulapalli, Raviteja, Chang, Jen-Hao Rick, Yang, Karren, Mantena, Gautam Varma, Tuzel, Oncel
While Automatic Speech Recognition (ASR) systems are widely used in many real-world applications, they often do not generalize well to new domains and need to be finetuned on data from these domains. However, target-domain data usually are not readil
Externí odkaz:
http://arxiv.org/abs/2309.10707
Autor:
Xian P. Yang, Yueh-Ting Yao, Pengyu Zheng, Shuyue Guan, Huibin Zhou, Tyler A. Cochran, Che-Min Lin, Jia-Xin Yin, Xiaoting Zhou, Zi-Jia Cheng, Zhaohu Li, Tong Shi, Md Shafayat Hossain, Shengwei Chi, Ilya Belopolski, Yu-Xiao Jiang, Maksim Litskevich, Gang Xu, Zhaoming Tian, Arun Bansil, Zhiping Yin, Shuang Jia, Tay-Rong Chang, M. Zahid Hasan
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-10 (2024)
Abstract The interplay of topology, magnetism, and correlations gives rise to intriguing phases of matter. In this study, through state-of-the-art angle-resolved photoemission spectroscopy, density functional theory, and dynamical mean-field theory c
Externí odkaz:
https://doaj.org/article/89cb4a9b6ca740499216d998aed435a2
Autor:
Qiangsheng Lu, P. V. Sreenivasa Reddy, Hoyeon Jeon, Alessandro R. Mazza, Matthew Brahlek, Weikang Wu, Shengyuan A. Yang, Jacob Cook, Clayton Conner, Xiaoqian Zhang, Amarnath Chakraborty, Yueh-Ting Yao, Hung-Ju Tien, Chun-Han Tseng, Po-Yuan Yang, Shang-Wei Lien, Hsin Lin, Tai-Chang Chiang, Giovanni Vignale, An-Ping Li, Tay-Rong Chang, Rob G. Moore, Guang Bian
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-9 (2024)
Abstract A two-dimensional (2D) Weyl semimetal, akin to a spinful variant of graphene, represents a topological matter characterized by Weyl fermion-like quasiparticles in low dimensions. The spinful linear band structure in two dimensions gives rise
Externí odkaz:
https://doaj.org/article/12cccee0e00d4596b5efea8882f27e56
Adapting generic speech recognition models to specific individuals is a challenging problem due to the scarcity of personalized data. Recent works have proposed boosting the amount of training data using personalized text-to-speech synthesis. Here, w
Externí odkaz:
http://arxiv.org/abs/2303.14885
Autor:
Huang, Chieh-Yang, Hsu, Ting-Yao, Rossi, Ryan, Nenkova, Ani, Kim, Sungchul, Chan, Gromit Yeuk-Yin, Koh, Eunyee, Giles, Clyde Lee, Huang, Ting-Hao 'Kenneth'
Good figure captions help paper readers understand complex scientific figures. Unfortunately, even published papers often have poorly written captions. Automatic caption generation could aid paper writers by providing good starting captions that can
Externí odkaz:
http://arxiv.org/abs/2302.12324
Publikováno v:
The Metropolitan Museum of Art Bulletin, 1916 Aug 01. 11(8), 176-178.
Externí odkaz:
https://www.jstor.org/stable/3254120
Autor:
陳秀玲 Hsiu-Ling Chen, 陳庭瑤 Ting-Yao Chen
Publikováno v:
Journal of Research in Education Sciences, Vol 69, Iss 2, Pp 243-273 (2024)
本研究旨在探討設計思考融入創意教學課程對師培生創造傾向、創意教學自我效能和設計思考力之影響。研究對象為臺灣北部某所科技大學選修創意教學策略之28位師培生,採用單一樣本前
Externí odkaz:
https://doaj.org/article/568576084b9940c8964b2d32f5263d68