Zobrazeno 1 - 10
of 105
pro vyhledávání: '"WANG Junda"'
Publikováno v:
Gongye shui chuli, Vol 44, Iss 3, Pp 97-103 (2024)
Using 304 stainless steel mesh as the outer support bed and serrated stainless steel mesh as the inner support structure，and filling coke in the serrated voids of the composite serrated mesh，a filled-bed electrode coupled with two materials of co
Externí odkaz:
https://doaj.org/article/f9f422eaf66741cd888bc2150ba41df5
Autor:
Wang, Junda, Li, Weijian, Wang, Han, Lyu, Hanjia, Thirukumaran, Caroline P., Mesfin, Addisu, Yu, Hong, Luo, Jiebo
Causal inference and model interpretability are gaining increasing attention, particularly in the biomedical domain. Despite recent advance, decorrelating features in nonlinear environments with human-interpretable representations remains underexplor
Externí odkaz:
http://arxiv.org/abs/2411.06338
Despite significant advancements, large language models (LLMs) still struggle with providing accurate answers when lacking domain-specific or up-to-date knowledge. Retrieval-Augmented Generation (RAG) addresses this limitation by incorporating extern
Externí odkaz:
http://arxiv.org/abs/2411.01705
Large language models (LLMs) have shown remarkable capabilities in various natural language processing tasks, yet they often struggle with maintaining factual accuracy, particularly in knowledge-intensive domains like healthcare. This study introduce
Externí odkaz:
http://arxiv.org/abs/2410.23526
Autor:
Wang, Junda, Ting, Yujan, Chen, Eric Z., Tran, Hieu, Yu, Hong, Huang, Weijing, Chen, Terrence
Multimodal large language models (MLLMs) have made significant strides, yet they face challenges in the medical domain due to limited specialized knowledge. While recent medical MLLMs demonstrate strong performance in lab settings, they often struggl
Externí odkaz:
http://arxiv.org/abs/2410.14948
Autor:
Yao, Zonghai, Zhang, Zihao, Tang, Chaolong, Bian, Xingyu, Zhao, Youxia, Yang, Zhichao, Wang, Junda, Zhou, Huixue, Jang, Won Seok, Ouyang, Feiyun, Yu, Hong
Artificial intelligence (AI) and large language models (LLMs) in healthcare require advanced clinical skills (CS), yet current benchmarks fail to evaluate these comprehensively. We introduce MedQA-CS, an AI-SCE framework inspired by medical education
Externí odkaz:
http://arxiv.org/abs/2410.01553
Publikováno v:
Zhongliu Fangzhi Yanjiu, Vol 48, Iss 4, Pp 429-432 (2021)
Externí odkaz:
https://doaj.org/article/79c39e6756dc4b70aa7625d0e563e640
Autor:
Vashisht, Parth, Lodha, Abhilasha, Maddipatla, Mukta, Yao, Zonghai, Mitra, Avijit, Yang, Zhichao, Wang, Junda, Kwon, Sunjae, Yu, Hong
This paper presents our team's participation in the MEDIQA-ClinicalNLP2024 shared task B. We present a novel approach to diagnosing clinical dermatology cases by integrating large multimodal models, specifically leveraging the capabilities of GPT-4V
Externí odkaz:
http://arxiv.org/abs/2404.17749
Autor:
Zhang, Zhe, Guan, Yifei, Wang, Junda, Apffel, Benjamin, Bossart, Aleksi, Qin, Haoye, Yazyev, Oleg V., Fleury, Romain
Exploring and understanding topological phases in systems with strong distributed disorder requires developing fundamentally new approaches to replace traditional tools such as topological band theory. Here, we present a general real-space renormaliz
Externí odkaz:
http://arxiv.org/abs/2404.15866
Large Language Models (LLMs) have demonstrated a remarkable potential in medical knowledge acquisition and question-answering. However, LLMs can potentially hallucinate and yield factually incorrect outcomes, even with domain-specific pretraining. Pr
Externí odkaz:
http://arxiv.org/abs/2402.17887