Zobrazeno 1 - 10
of 806
pro vyhledávání: '"Yu, Neng"'
Autor:
Yuan, Jiayi, Liu, Hongyi, Shaochen, Zhong, Chuang, Yu-Neng, Li, Songchen, Wang, Guanchu, Le, Duy, Jin, Hongye, Chaudhary, Vipin, Xu, Zhaozhuo, Liu, Zirui, Hu, Xia
Long context capability is a crucial competency for large language models (LLMs) as it mitigates the human struggle to digest long-form texts. This capability enables complex task-solving scenarios such as book summarization, code assistance, and man
Externí odkaz:
http://arxiv.org/abs/2407.01527
Autor:
Chuang, Yu-Neng, Li, Songchen, Yuan, Jiayi, Wang, Guanchu, Lai, Kwei-Herng, Yu, Leisheng, Ding, Sirui, Chang, Chia-Yuan, Tan, Qiaoyu, Zha, Daochen, Hu, Xia
Inspired by Large Language Models (LLMs), Time Series Forecasting (TSF), a long-standing task in time series analysis, is undergoing a transition towards Large Time Series Models (LTSMs), aiming to train universal transformer-based models for TSF. Ho
Externí odkaz:
http://arxiv.org/abs/2406.14045
Foundation Models (FMs) serve as a general class for the development of artificial intelligence systems, offering broad potential for generalization across a spectrum of downstream tasks. Despite extensive research into self-supervised learning as th
Externí odkaz:
http://arxiv.org/abs/2406.08310
Autor:
Liu, Hongyi, Liu, Zirui, Tang, Ruixiang, Yuan, Jiayi, Zhong, Shaochen, Chuang, Yu-Neng, Li, Li, Chen, Rui, Hu, Xia
Fine-tuning LLMs is crucial to enhancing their task-specific performance and ensuring model behaviors are aligned with human preferences. Among various fine-tuning methods, LoRA is popular for its efficiency and ease to use, allowing end-users to eas
Externí odkaz:
http://arxiv.org/abs/2403.00108
Large language models (LLMs) are great at processing multiple natural language processing tasks, but their abilities are constrained by inferior performance with long context, slow inference speed, and the high cost of computing the results. Deployin
Externí odkaz:
http://arxiv.org/abs/2402.18700
Autor:
Chuang, Yu-Neng, Wang, Guanchu, Chang, Chia-Yuan, Tang, Ruixiang, Zhong, Shaochen, Yang, Fan, Du, Mengnan, Cai, Xuanting, Hu, Xia
Large Language Models (LLMs) have become proficient in addressing complex tasks by leveraging their extensive internal knowledge and reasoning capabilities. However, the black-box nature of these models complicates the task of explaining their decisi
Externí odkaz:
http://arxiv.org/abs/2402.04678
Autor:
Chen, Aokun, Li, Qian, Huang, Yu, Li, Yongqiu, Chuang, Yu-neng, Hu, Xia, Guo, Serena, Wu, Yonghui, Guo, Yi, Bian, Jiang
A comprehensive view of factors associated with AD/ADRD will significantly aid in studies to develop new treatments for AD/ADRD and identify high-risk populations and patients for prevention efforts. In our study, we summarized the risk factors for A
Externí odkaz:
http://arxiv.org/abs/2402.15515
Autor:
Wang, Guanchu, Chuang, Yu-Neng, Yang, Fan, Du, Mengnan, Chang, Chia-Yuan, Zhong, Shaochen, Liu, Zirui, Xu, Zhaozhuo, Zhou, Kaixiong, Cai, Xuanting, Hu, Xia
Explainable machine learning significantly improves the transparency of deep neural networks. However, existing work is constrained to explaining the behavior of individual model predictions, and lacks the ability to transfer the explanation across v
Externí odkaz:
http://arxiv.org/abs/2312.15359
In real-world applications, machine learning models often become obsolete due to shifts in the joint distribution arising from underlying temporal trends, a phenomenon known as the "concept drift". Existing works propose model-specific strategies to
Externí odkaz:
http://arxiv.org/abs/2310.01508
Previous efforts on reconfigurable analog circuits mostly focused on specialized analog circuits, produced through careful co-design, or on highly reconfigurable, but relatively resource inefficient, accelerators that implement analog compute paradig
Externí odkaz:
http://arxiv.org/abs/2309.08774