Zobrazeno 1 - 10
of 297
pro vyhledávání: '"Chen, RuiJun"'
While training large language models (LLMs) from scratch can indeed lead to models with distinct capabilities and strengths, it incurs substantial costs and may lead to redundancy in competencies. Knowledge fusion aims to integrate existing LLMs of d
Externí odkaz:
http://arxiv.org/abs/2408.07990
The alignment of large language models (LLMs) is crucial not only for unlocking their potential in specific tasks but also for ensuring that responses meet human expectations and adhere to safety and ethical principles. Current alignment methodologie
Externí odkaz:
http://arxiv.org/abs/2406.10813
With the rapid growth in the size and complexity of large language models (LLMs), the costs associated with their training and inference have escalated significantly. Research indicates that certain layers in LLMs harbor substantial redundancy, and p
Externí odkaz:
http://arxiv.org/abs/2406.10594
Autor:
Thompson, Will E., Vidmar, David M., De Freitas, Jessica K., Pfeifer, John M., Fornwalt, Brandon K., Chen, Ruijun, Altay, Gabriel, Manghnani, Kabir, Nelsen, Andrew C., Morland, Kellie, Stumpe, Martin C., Miotto, Riccardo
Identifying disease phenotypes from electronic health records (EHRs) is critical for numerous secondary uses. Manually encoding physician knowledge into rules is particularly challenging for rare diseases due to inadequate EHR coding, necessitating r
Externí odkaz:
http://arxiv.org/abs/2312.06457
Maintaining engagement and consistency is particularly important in dialogue systems. Existing works have improved the performance of dialogue systems by intentionally learning interlocutor personas with sophisticated network structures. One issue wi
Externí odkaz:
http://arxiv.org/abs/2301.04871
Publikováno v:
In Building and Environment 1 January 2025 267 Part A
Autor:
Pang, Chao, Jiang, Xinzhuo, Kalluri, Krishna S, Spotnitz, Matthew, Chen, RuiJun, Perotte, Adler, Natarajan, Karthik
Publikováno v:
Proceedings of Machine Learning for Health, PMLR 158:239-260, 2021
Embedding algorithms are increasingly used to represent clinical concepts in healthcare for improving machine learning tasks such as clinical phenotyping and disease prediction. Recent studies have adapted state-of-the-art bidirectional encoder repre
Externí odkaz:
http://arxiv.org/abs/2111.08585
Autor:
Huang, Xin, Tang, Xuejiao, Zhang, Wenbin, Pei, Shichao, Zhang, Ji, Zhang, Mingli, Liu, Zhen, Chen, Ruijun, Huang, Yiyi
In this paper, we design and implement a generic medical knowledge based system (MKBS) for identifying diseases from several symptoms. In this system, some important aspects like knowledge bases system, knowledge representation, inference engine have
Externí odkaz:
http://arxiv.org/abs/2110.04439
Publikováno v:
In Urban Climate May 2024 55