Zobrazeno 1 - 10
of 67
pro vyhledávání: '"Qin, Yulei"'
Autor:
Yang, Yuncheng, Qin, Yulei, Wu, Tong, Xu, Zihan, Li, Gang, Guo, Pengcheng, Shao, Hang, Shi, Yuchen, Li, Ke, Sun, Xing, Yang, Jie, Gu, Yun
The cultivation of expertise for large language models (LLMs) to solve tasks of specific areas often requires special-purpose tuning with calibrated behaviors on the expected stable outputs. To avoid huge cost brought by manual preparation of instruc
Externí odkaz:
http://arxiv.org/abs/2408.15915
Autor:
Qin, Yulei, Yang, Yuncheng, Guo, Pengcheng, Li, Gang, Shao, Hang, Shi, Yuchen, Xu, Zihan, Gu, Yun, Li, Ke, Sun, Xing
Instruction tuning plays a critical role in aligning large language models (LLMs) with human preference. Despite the vast amount of open instruction datasets, naively training a LLM on all existing instructions may not be optimal and practical. To pi
Externí odkaz:
http://arxiv.org/abs/2408.02085
Autor:
Yang, Yuncheng, Zhang, Chuyan, Yang, Zuopeng, Gao, Yuting, Qin, Yulei, Li, Ke, Sun, Xing, Yang, Jie, Gu, Yun
Prompt learning is effective for fine-tuning foundation models to improve their generalization across a variety of downstream tasks. However, the prompts that are independently optimized along a single modality path, may sacrifice the vision-language
Externí odkaz:
http://arxiv.org/abs/2403.06136
Autor:
Cui, Xiao, Qin, Yulei, Gao, Yuting, Zhang, Enwei, Xu, Zihan, Wu, Tong, Li, Ke, Sun, Xing, Zhou, Wengang, Li, Houqiang
Knowledge distillation (KD) has been widely adopted to compress large language models (LLMs). Existing KD methods investigate various divergence measures including the Kullback-Leibler (KL), reverse Kullback-Leibler (RKL), and Jensen-Shannon (JS) div
Externí odkaz:
http://arxiv.org/abs/2402.17110
Retrieval augmentation has become an effective solution to empower large language models (LLMs) with external and verified knowledge sources from the database, which overcomes the limitations and hallucinations of LLMs in handling up-to-date and doma
Externí odkaz:
http://arxiv.org/abs/2311.11691
Autor:
Qin, Yulei, Chen, Xingyu, Shen, Yunhang, Fu, Chaoyou, Gu, Yun, Li, Ke, Sun, Xing, Ji, Rongrong
Webly supervised learning has attracted increasing attention for its effectiveness in exploring publicly accessible data at scale without manual annotation. However, most existing methods of learning with web datasets are faced with challenges from l
Externí odkaz:
http://arxiv.org/abs/2310.09761
Autor:
Fu, Chaoyou, Chen, Peixian, Shen, Yunhang, Qin, Yulei, Zhang, Mengdan, Lin, Xu, Yang, Jinrui, Zheng, Xiawu, Li, Ke, Sun, Xing, Wu, Yunsheng, Ji, Rongrong
Multimodal Large Language Model (MLLM) relies on the powerful LLM to perform multimodal tasks, showing amazing emergent abilities in recent studies, such as writing poems based on an image. However, it is difficult for these case studies to fully ref
Externí odkaz:
http://arxiv.org/abs/2306.13394
Autor:
Zhang, Minghui, Wu, Yangqian, Zhang, Hanxiao, Qin, Yulei, Zheng, Hao, Tang, Wen, Arnold, Corey, Pei, Chenhao, Yu, Pengxin, Nan, Yang, Yang, Guang, Walsh, Simon, Marshall, Dominic C., Komorowski, Matthieu, Wang, Puyang, Guo, Dazhou, Jin, Dakai, Wu, Ya'nan, Zhao, Shuiqing, Chang, Runsheng, Zhang, Boyu, Lv, Xing, Qayyum, Abdul, Mazher, Moona, Su, Qi, Wu, Yonghuang, Liu, Ying'ao, Zhu, Yufei, Yang, Jiancheng, Pakzad, Ashkan, Rangelov, Bojidar, Estepar, Raul San Jose, Espinosa, Carlos Cano, Sun, Jiayuan, Yang, Guang-Zhong, Gu, Yun
Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image r
Externí odkaz:
http://arxiv.org/abs/2303.05745
Autor:
Qin, Yulei, Chen, Xingyu, Chen, Chao, Shen, Yunhang, Ren, Bo, Gu, Yun, Yang, Jie, Shen, Chunhua
Recently, webly supervised learning (WSL) has been studied to leverage numerous and accessible data from the Internet. Most existing methods focus on learning noise-robust models from web images while neglecting the performance drop caused by the dif
Externí odkaz:
http://arxiv.org/abs/2212.00465
Autor:
Zhang, Hanxiao, Chen, Liang, Gu, Xiao, Zhang, Minghui, Qin, Yulei, Yao, Feng, Wang, Zhexin, Gu, Yun, Yang, Guang-Zhong
Recent evolution in deep learning has proven its value for CT-based lung nodule classification. Most current techniques are intrinsically black-box systems, suffering from two generalizability issues in clinical practice. First, benign-malignant disc
Externí odkaz:
http://arxiv.org/abs/2202.12515