Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Nie, Yuxiang"'
Autor:
He, Sunan, Nie, Yuxiang, Wang, Hongmei, Yang, Shu, Wang, Yihui, Cai, Zhiyuan, Chen, Zhixuan, Xu, Yingxue, Luo, Luyang, Xiang, Huiling, Lin, Xi, Wu, Mingxiang, Peng, Yifan, Shih, George, Xu, Ziyang, Wu, Xian, Wang, Qiong, Chan, Ronald Cheong Kin, Vardhanabhuti, Varut, Chu, Winnie Chiu Wing, Zheng, Yefeng, Rajpurkar, Pranav, Zhang, Kang, Chen, Hao
Generalist foundation models (GFMs) are renowned for their exceptional capability and flexibility in effectively generalizing across diverse tasks and modalities. In the field of medicine, while GFMs exhibit superior generalizability based on their e
Externí odkaz:
http://arxiv.org/abs/2404.15127
Autor:
He, Yuting, Huang, Fuxiang, Jiang, Xinrui, Nie, Yuxiang, Wang, Minghao, Wang, Jiguang, Chen, Hao
Foundation model, which is pre-trained on broad data and is able to adapt to a wide range of tasks, is advancing healthcare. It promotes the development of healthcare artificial intelligence (AI) models, breaking the contradiction between limited AI
Externí odkaz:
http://arxiv.org/abs/2404.03264
Mixed initiative serves as one of the key factors in controlling conversation directions. For a speaker, responding passively or leading proactively would result in rather different responses. However, most dialogue systems focus on training a holist
Externí odkaz:
http://arxiv.org/abs/2403.17636
Multi-modal Large Language Models (MLLMs) have demonstrated their ability to perceive objects in still images, but their application in video-related tasks, such as object tracking, remains understudied. This lack of exploration is primarily due to t
Externí odkaz:
http://arxiv.org/abs/2403.16558
Scientific machine reading comprehension (SMRC) aims to understand scientific texts through interactions with humans by given questions. As far as we know, there is only one dataset focused on exploring full-text scientific machine reading comprehens
Externí odkaz:
http://arxiv.org/abs/2306.14149
Annotating long-document question answering (long-document QA) pairs is time-consuming and expensive. To alleviate the problem, it might be possible to generate long-document QA pairs via unsupervised question answering (UQA) methods. However, existi
Externí odkaz:
http://arxiv.org/abs/2305.02235
Long document question answering is a challenging task due to its demands for complex reasoning over long text. Previous works usually take long documents as non-structured flat texts or only consider the local structure in long documents. However, t
Externí odkaz:
http://arxiv.org/abs/2210.05499
Unsupervised question answering is an attractive task due to its independence on labeled data. Previous works usually make use of heuristic rules as well as pre-trained models to construct data and train QA models. However, most of these works regard
Externí odkaz:
http://arxiv.org/abs/2208.10813
Autor:
Hu, Yong, Huang, Heyan, Lan, Tian, Wei, Xiaochi, Nie, Yuxiang, Qi, Jiarui, Yang, Liner, Mao, Xian-Ling
Second language acquisition (SLA) modeling is to predict whether second language learners could correctly answer the questions according to what they have learned. It is a fundamental building block of the personalized learning system and has attract
Externí odkaz:
http://arxiv.org/abs/1908.09283
Autor:
Dong, Lisong, Zhou, Xiaoyao, Zheng, Shuxin, Luo, Zhongfan, Nie, Yuxiang, Feng, Xin, Zhu, Jiahua, Wang, Zhangzhong, Lu, Xiaohua, Mu, Liwen
Publikováno v:
In Chemical Engineering Journal 15 February 2023 458
