Zobrazeno 1 - 10
of 12 072
pro vyhledávání: '"XU, Hua"'
Autor:
Zhang, Xingjian, Xiong, Ziyang, Liu, Shixuan, Xie, Yutong, Ergen, Tolga, Shim, Dongsub, Xu, Hua, Lee, Honglak, Me, Qiaozhu
Low-dimensional visualizations, or "projection maps" of datasets, are widely used across scientific research and creative industries as effective tools for interpreting large-scale and complex information. These visualizations not only support unders
Externí odkaz:
http://arxiv.org/abs/2412.18673
Autor:
Ji, Jiaming, Zhou, Jiayi, Lou, Hantao, Chen, Boyuan, Hong, Donghai, Wang, Xuyao, Chen, Wenqi, Wang, Kaile, Pan, Rui, Li, Jiahao, Wang, Mohan, Dai, Josef, Qiu, Tianyi, Xu, Hua, Li, Dong, Chen, Weipeng, Song, Jun, Zheng, Bo, Yang, Yaodong
Reinforcement learning from human feedback (RLHF) has proven effective in enhancing the instruction-following capabilities of large language models; however, it remains underexplored in the cross-modality domain. As the number of modalities increases
Externí odkaz:
http://arxiv.org/abs/2412.15838
Multimodal intent understanding is a significant research area that requires effectively leveraging multiple modalities to analyze human language. Existing methods face two main challenges in this domain. Firstly, they have limitations in capturing n
Externí odkaz:
http://arxiv.org/abs/2412.12453
Autor:
Wang, Yan, Huang, Jimin, He, Huan, Zhang, Vincent, Zhou, Yujia, Hao, Xubing, Ram, Pritham, Qian, Lingfei, Xie, Qianqian, Weng, Ruey-Ling, Lin, Fongci, Hu, Yan, Cui, Licong, Jiang, Xiaoqian, Xu, Hua, Hong, Na
Common Data Elements (CDEs) standardize data collection and sharing across studies, enhancing data interoperability and improving research reproducibility. However, implementing CDEs presents challenges due to the broad range and variety of data elem
Externí odkaz:
http://arxiv.org/abs/2412.00491
Artificial Intelligence has proven to be a transformative tool for advancing scientific research across a wide range of disciplines. However, a significant gap still exists between AI and scientific communities, limiting the full potential of AI meth
Externí odkaz:
http://arxiv.org/abs/2412.09628
Autor:
Hu, Yan, Zuo, Xu, Zhou, Yujia, Peng, Xueqing, Huang, Jimin, Keloth, Vipina K., Zhang, Vincent J., Weng, Ruey-Ling, Chen, Qingyu, Jiang, Xiaoqian, Roberts, Kirk E., Xu, Hua
Backgrounds: Information extraction (IE) is critical in clinical natural language processing (NLP). While large language models (LLMs) excel on generative tasks, their performance on extractive tasks remains debated. Methods: We investigated Named En
Externí odkaz:
http://arxiv.org/abs/2411.10020
Generating discharge summaries is a crucial yet time-consuming task in clinical practice, essential for conveying pertinent patient information and facilitating continuity of care. Recent advancements in large language models (LLMs) have significantl
Externí odkaz:
http://arxiv.org/abs/2411.03805
Autor:
Fleurence, Rachael, Wang, Xiaoyan, Bian, Jiang, Higashi, Mitchell K., Ayer, Turgay, Xu, Hua, Dawoud, Dalia, Chhatwal, Jagpreet
Objective: This article offers a taxonomy of generative artificial intelligence (AI) for health economics and outcomes research (HEOR), explores its emerging applications, and outlines methods to enhance the accuracy and reliability of AI-generated o
Externí odkaz:
http://arxiv.org/abs/2410.20204
By leveraging GPT-4 for ontology narration, we developed GPTON to infuse structured knowledge into LLMs through verbalized ontology terms, achieving accurate text and ontology annotations for over 68% of gene sets in the top five predictions. Manual
Externí odkaz:
http://arxiv.org/abs/2410.10899
Quantum ant colony optimization (QACO) has drew much attention since it combines the advantages of quantum computing and ant colony optimization (ACO) algorithm overcoming some limitations of the traditional ACO algorithm. However,due to the hardware
Externí odkaz:
http://arxiv.org/abs/2410.17277