Zobrazeno 1 - 10
of 514
pro vyhledávání: '"Wang, Peiqi"'
Autor:
Wang, Peiqi, Shen, Yikang, Guo, Zhen, Stallone, Matthew, Kim, Yoon, Golland, Polina, Panda, Rameswar
We aim to select data subsets for the fine-tuning of large language models to more effectively follow instructions. Prior work has emphasized the importance of diversity in dataset curation but relied on heuristics such as the number of tasks. In thi
Externí odkaz:
http://arxiv.org/abs/2402.02318
Large Language Models (LLMs) have made remarkable advancements in the field of natural language processing. However, their increasing size poses challenges in terms of computational cost. On the other hand, Small Language Models (SLMs) are known for
Externí odkaz:
http://arxiv.org/abs/2305.07804
Autor:
Zhang, Dehui, Xu, Dong, Li, Yuhang, Luo, Yi, Hu, Jingtian, Zhou, Jingxuan, Zhang, Yucheng, Zhou, Boxuan, Wang, Peiqi, Li, Xurong, Bai, Bijie, Ren, Huaying, Wang, Laiyuan, Jarrahi, Mona, Huang, Yu, Ozcan, Aydogan, Duan, Xiangfeng
Publikováno v:
Nature Communications (2024)
Nonlinear optical processing of ambient natural light is highly desired in computational imaging and sensing applications. A strong optical nonlinear response that can work under weak broadband incoherent light is essential for this purpose. Here we
Externí odkaz:
http://arxiv.org/abs/2304.13298
Autor:
Wang, Peiqi, Liu, Yingcheng, Ko, Ching-Yun, Wells, William M., Berkowitz, Seth, Horng, Steven, Golland, Polina
Self-supervised representation learning on image-text data facilitates crucial medical applications, such as image classification, visual grounding, and cross-modal retrieval. One common approach involves contrasting semantically similar (positive) a
Externí odkaz:
http://arxiv.org/abs/2304.13181
Image-text multimodal representation learning aligns data across modalities and enables important medical applications, e.g., image classification, visual grounding, and cross-modal retrieval. In this work, we establish a connection between multimoda
Externí odkaz:
http://arxiv.org/abs/2212.05561
Autor:
Wang, Peiqi
Adoption of machine learning models in healthcare requires end users’ trust in the system. Models that provide additional supportive evidence for their predictions promise to facilitate adoption. We define consistent evidence to be both compatible
Externí odkaz:
https://hdl.handle.net/1721.1/143207
Autor:
Cheng, Zhihui, Pang, Chin-Sheng, Wang, Peiqi, Le, Son T., Wu, Yanqing, Shahrjerdi, Davood, Radu, Iuliana, Lemme, Max C., Peng, Lian-Mao, Duan, Xiangfeng, Chen, Zhihong, Appenzeller, Joerg, Koester, Steven J., Pop, Eric, Franklin, Aaron D., Richter, Curt A.
Publikováno v:
Nature Electronics 5 (2022) 416-423
Emerging low-dimensional nanomaterials have been studied for decades in device applications as field-effect transistors (FETs). However, properly reporting and comparing device performance has been challenging due to the involvement and interlinking
Externí odkaz:
http://arxiv.org/abs/2203.16759
Publikováno v:
In Journal of Dentistry August 2024 147
Autor:
Zou, Zhaowei, Liu, Xiu, Yu, Jie, Ban, Tao, Zhang, Ziyi, Wang, Peiqi, Huang, Renli, Zheng, Fuxin, Chang, Yafei, Peng, Wanli, Tang, Yubo, Feng, Xiaoqing, Zhao, Ziying, Lv, Xiaofei, Huang, Shuai, Guo, Jiawei, Tuo, Yonghua, Zhou, Zhijun, Liang, Sijia
Publikováno v:
In Journal of Hepatology June 2024 80(6):834-845
Adoption of machine learning models in healthcare requires end users' trust in the system. Models that provide additional supportive evidence for their predictions promise to facilitate adoption. We define consistent evidence to be both compatible an
Externí odkaz:
http://arxiv.org/abs/2111.07048