Zobrazeno 1 - 10
of 6 030
pro vyhledávání: '"LU, Chang"'
Autor:
Beigi, Mohammad, Wang, Sijia, Shen, Ying, Lin, Zihao, Kulkarni, Adithya, He, Jianfeng, Chen, Feng, Jin, Ming, Cho, Jin-Hee, Zhou, Dawei, Lu, Chang-Tien, Huang, Lifu
In recent years, Large Language Models (LLMs) have become fundamental to a broad spectrum of artificial intelligence applications. As the use of LLMs expands, precisely estimating the uncertainty in their predictions has become crucial. Current metho
Externí odkaz:
http://arxiv.org/abs/2410.20199
Electronic Health Records (EHR) has revolutionized healthcare data management and prediction in the field of AI and machine learning. Accurate predictions of diagnosis and medications significantly mitigate health risks and provide guidance for preve
Externí odkaz:
http://arxiv.org/abs/2410.19955
Autor:
Wang, Shengkun, Ji, Taoran, Wang, Linhan, Sun, Yanshen, Liu, Shang-Ching, Kumar, Amit, Lu, Chang-Tien
The stock price prediction task holds a significant role in the financial domain and has been studied for a long time. Recently, large language models (LLMs) have brought new ways to improve these predictions. While recent financial large language mo
Externí odkaz:
http://arxiv.org/abs/2409.08281
Autor:
Wang, Shengkun, Ji, Taoran, He, Jianfeng, Almutairi, Mariam, Wang, Dan, Wang, Linhan, Zhang, Min, Lu, Chang-Tien
Stock volatility prediction is an important task in the financial industry. Recent advancements in multimodal methodologies, which integrate both textual and auditory data, have demonstrated significant improvements in this domain, such as earnings c
Externí odkaz:
http://arxiv.org/abs/2407.18324
Autor:
He, Jianfeng, Yang, Runing, Yu, Linlin, Li, Changbin, Jia, Ruoxi, Chen, Feng, Jin, Ming, Lu, Chang-Tien
Text summarization, a key natural language generation (NLG) task, is vital in various domains. However, the high cost of inaccurate summaries in risk-critical applications, particularly those involving human-in-the-loop decision-making, raises concer
Externí odkaz:
http://arxiv.org/abs/2406.17274
Autor:
Beigi, Mohammad, Shen, Ying, Yang, Runing, Lin, Zihao, Wang, Qifan, Mohan, Ankith, He, Jianfeng, Jin, Ming, Lu, Chang-Tien, Huang, Lifu
Despite their vast capabilities, Large Language Models (LLMs) often struggle with generating reliable outputs, frequently producing high-confidence inaccuracies known as hallucinations. Addressing this challenge, our research introduces InternalInspe
Externí odkaz:
http://arxiv.org/abs/2406.12053
Autor:
Hu, Hao-Chun, Chang, Shyue-Yih, Wang, Chuen-Heng, Li, Kai-Jun, Cho, Hsiao-Yun, Chen, Yi-Ting, Lu, Chang-Jung, Tsai, Tzu-Pei, Lee, Oscar Kuang-Sheng
Publikováno v:
Journal of Medical Internet Research, Vol 23, Iss 6, p e25247 (2021)
BackgroundDysphonia influences the quality of life by interfering with communication. However, a laryngoscopic examination is expensive and not readily accessible in primary care units. Experienced laryngologists are required to achieve an accurate d
Externí odkaz:
https://doaj.org/article/e5c00ea4b4c54dc280949a2b2f838d45
Autor:
Wang, Linhan, Cheng, Kai, Lei, Shuo, Wang, Shengkun, Yin, Wei, Lei, Chenyang, Long, Xiaoxiao, Lu, Chang-Tien
We present DC-Gaussian, a new method for generating novel views from in-vehicle dash cam videos. While neural rendering techniques have made significant strides in driving scenarios, existing methods are primarily designed for videos collected by aut
Externí odkaz:
http://arxiv.org/abs/2405.17705
Network interdiction problems are combinatorial optimization problems involving two players: one aims to solve an optimization problem on a network, while the other seeks to modify the network to thwart the first player's objectives. Such problems ty
Externí odkaz:
http://arxiv.org/abs/2405.16409
Autor:
Wang, Zaitian, Wang, Pengfei, Liu, Kunpeng, Wang, Pengyang, Fu, Yanjie, Lu, Chang-Tien, Aggarwal, Charu C., Pei, Jian, Zhou, Yuanchun
Data augmentation is a series of techniques that generate high-quality artificial data by manipulating existing data samples. By leveraging data augmentation techniques, AI models can achieve significantly improved applicability in tasks involving sc
Externí odkaz:
http://arxiv.org/abs/2405.09591