Zobrazeno 1 - 10
of 3 034
pro vyhledávání: '"ZHANG, YUWEI"'
Recent large language model (LLM)-driven chat assistant systems have integrated memory components to track user-assistant chat histories, enabling more accurate and personalized responses. However, their long-term memory capabilities in sustained int
Externí odkaz:
http://arxiv.org/abs/2410.10813
The high incidence and mortality rates associated with respiratory diseases underscores the importance of early screening. Machine learning models can automate clinical consultations and auscultation, offering vital support in this area. However, the
Externí odkaz:
http://arxiv.org/abs/2410.05361
Option pricing models, essential in financial mathematics and risk management, have been extensively studied and recently advanced by AI methodologies. However, American option pricing remains challenging due to the complexity of determining optimal
Externí odkaz:
http://arxiv.org/abs/2409.18168
Human Activity Recognition (HAR) has gained great attention from researchers due to the popularity of mobile devices and the need to observe users' daily activity data for better human-computer interaction. In this work, we collect a human activity r
Externí odkaz:
http://arxiv.org/abs/2409.16730
This paper advances a novel architectural schema anchored upon the Transformer paradigm and innovatively amalgamates the K-means categorization algorithm to augment the contextual apprehension capabilities of the schema. The transformer model perform
Externí odkaz:
http://arxiv.org/abs/2408.04216
Autor:
Wang, Zilong, Wang, Zifeng, Le, Long, Zheng, Huaixiu Steven, Mishra, Swaroop, Perot, Vincent, Zhang, Yuwei, Mattapalli, Anush, Taly, Ankur, Shang, Jingbo, Lee, Chen-Yu, Pfister, Tomas
Retrieval augmented generation (RAG) combines the generative abilities of large language models (LLMs) with external knowledge sources to provide more accurate and up-to-date responses. Recent RAG advancements focus on improving retrieval outcomes th
Externí odkaz:
http://arxiv.org/abs/2407.08223
Autor:
Zhang, Yuwei, Xia, Tong, Han, Jing, Wu, Yu, Rizos, Georgios, Liu, Yang, Mosuily, Mohammed, Chauhan, Jagmohan, Mascolo, Cecilia
Respiratory audio, such as coughing and breathing sounds, has predictive power for a wide range of healthcare applications, yet is currently under-explored. The main problem for those applications arises from the difficulty in collecting large labele
Externí odkaz:
http://arxiv.org/abs/2406.16148
Autor:
Liu, Yanming, Peng, Xinyue, Zhang, Yuwei, Ke, Xiaolan, Deng, Songhang, Cao, Jiannan, Ma, Chen, Fu, Mengchen, Zhang, Xuhong, Cheng, Sheng, Wang, Xun, Yin, Jianwei, Du, Tianyu
Large language models have repeatedly shown outstanding performance across diverse applications. However, deploying these models can inadvertently risk user privacy. The significant memory demands during training pose a major challenge in terms of re
Externí odkaz:
http://arxiv.org/abs/2406.11087
Autor:
Wan, Jixiang, Zhang, Xudong, Dong, Shuzhou, Zhang, Yuwei, Yang, Yuchen, Wu, Ruoxi, Jiang, Ye, Li, Jijunnan, Lin, Jinquan, Yang, Ming
Accurate and robust localization remains a significant challenge for autonomous vehicles. The cost of sensors and limitations in local computational efficiency make it difficult to scale to large commercial applications. Traditional vision-based appr
Externí odkaz:
http://arxiv.org/abs/2406.03835
Autor:
Liu, Yanming, Peng, Xinyue, Cao, Jiannan, Bo, Shi, Zhang, Yuwei, Zhang, Xuhong, Cheng, Sheng, Wang, Xun, Yin, Jianwei, Du, Tianyu
Large language models (LLMs) have demonstrated exceptional reasoning capabilities, enabling them to solve various complex problems. Recently, this ability has been applied to the paradigm of tool learning. Tool learning involves providing examples of
Externí odkaz:
http://arxiv.org/abs/2406.03807