Zobrazeno 1 - 10
of 896
pro vyhledávání: '"Yanda, P"'
Autor:
Huang, Baoru, Vo, Tuan, Kongtongvattana, Chayun, Dagnino, Giulio, Kundrat, Dennis, Chi, Wenqiang, Abdelaziz, Mohamed, Kwok, Trevor, Jianu, Tudor, Do, Tuong, Le, Hieu, Nguyen, Minh, Nguyen, Hoan, Tjiputra, Erman, Tran, Quang, Xie, Jianyang, Meng, Yanda, Bhattarai, Binod, Tan, Zhaorui, Liu, Hongbin, Gan, Hong Seng, Wang, Wei, Yang, Xi, Wang, Qiufeng, Su, Jionglong, Huang, Kaizhu, Stefanidis, Angelos, Guo, Min, Du, Bo, Tao, Rong, Vu, Minh, Zheng, Guoyan, Zheng, Yalin, Vasconcelos, Francisco, Stoyanov, Danail, Elson, Daniel, Baena, Ferdinando Rodriguez y, Nguyen, Anh
Real-time visual feedback from catheterization analysis is crucial for enhancing surgical safety and efficiency during endovascular interventions. However, existing datasets are often limited to specific tasks, small scale, and lack the comprehensive
Externí odkaz:
http://arxiv.org/abs/2408.13126
With the advancement of Multimodal Large Language Models (MLLM), LLM-driven visual agents are increasingly impacting software interfaces, particularly those with graphical user interfaces. This work introduces a novel LLM-based multimodal agent frame
Externí odkaz:
http://arxiv.org/abs/2408.11824
In this study, we explore an emerging research area of Continual Learning for Temporal Sensitive Question Answering (CLTSQA). Previous research has primarily focused on Temporal Sensitive Question Answering (TSQA), often overlooking the unpredictable
Externí odkaz:
http://arxiv.org/abs/2407.12470
Autor:
Zeng, Qingcheng, Jin, Mingyu, Yu, Qinkai, Wang, Zhenting, Hua, Wenyue, Zhou, Zihao, Sun, Guangyan, Meng, Yanda, Ma, Shiqing, Wang, Qifan, Juefei-Xu, Felix, Ding, Kaize, Yang, Fan, Tang, Ruixiang, Zhang, Yongfeng
Large Language Models (LLMs) are employed across various high-stakes domains, where the reliability of their outputs is crucial. One commonly used method to assess the reliability of LLMs' responses is uncertainty estimation, which gauges the likelih
Externí odkaz:
http://arxiv.org/abs/2407.11282
Autor:
Yu, Qinkai, Xie, Jianyang, Nguyen, Anh, Zhao, He, Zhang, Jiong, Fu, Huazhu, Zhao, Yitian, Zheng, Yalin, Meng, Yanda
Diabetic retinopathy (DR) is a complication of diabetes and usually takes decades to reach sight-threatening levels. Accurate and robust detection of DR severity is critical for the timely management and treatment of diabetes. However, most current D
Externí odkaz:
http://arxiv.org/abs/2407.04068
Autor:
Ding, Zepeng, Ke, Ruiyang, Huang, Wenhao, Jiang, Guochao, Li, Yanda, Yang, Deqing, Liang, Jiaqing
Existing research on large language models (LLMs) shows that they can solve information extraction tasks through multi-step planning. However, their extraction behavior on complex sentences and tasks is unstable, emerging issues such as false positiv
Externí odkaz:
http://arxiv.org/abs/2406.11455
Autor:
Wang, Meng, Lin, Tian, Lin, Aidi, Yu, Kai, Peng, Yuanyuan, Wang, Lianyu, Chen, Cheng, Zou, Ke, Liang, Huiyu, Chen, Man, Yao, Xue, Zhang, Meiqin, Huang, Binwei, Zheng, Chaoxin, Zhang, Peixin, Chen, Wei, Luo, Yilong, Chen, Yifan, Xia, Honghe, Shi, Tingkun, Zhang, Qi, Guo, Jinming, Chen, Xiaolin, Wang, Jingcheng, Tham, Yih Chung, Liu, Dianbo, Wong, Wendy, Thakur, Sahil, Fenner, Beau, Fang, Danqi, Liu, Siying, Liu, Qingyun, Huang, Yuqiang, Zeng, Hongqiang, Meng, Yanda, Zhou, Yukun, Jiang, Zehua, Qiu, Minghui, Zhang, Changqing, Chen, Xinjian, Wang, Sophia Y, Lee, Cecilia S, Sobrin, Lucia, Cheung, Carol Y, Pang, Chi Pui, Keane, Pearse A, Cheng, Ching-Yu, Chen, Haoyu, Fu, Huazhu
Previous foundation models for retinal images were pre-trained with limited disease categories and knowledge base. Here we introduce RetiZero, a vision-language foundation model that leverages knowledge from over 400 fundus diseases. To RetiZero's pr
Externí odkaz:
http://arxiv.org/abs/2406.09317
Autor:
Wang, Dixuan, Li, Yanda, Jiang, Junyuan, Ding, Zepeng, Jiang, Guochao, Liang, Jiaqing, Yang, Deqing
Large Language Models (LLMs) have shown remarkable capabilities in language understanding and generation. Nonetheless, it was also witnessed that LLMs tend to produce inaccurate responses to specific queries. This deficiency can be traced to the toke
Externí odkaz:
http://arxiv.org/abs/2405.17067
Autor:
Jin, Mingyu, Yu, Qinkai, Huang, Jingyuan, Zeng, Qingcheng, Wang, Zhenting, Hua, Wenyue, Zhao, Haiyan, Mei, Kai, Meng, Yanda, Ding, Kaize, Yang, Fan, Du, Mengnan, Zhang, Yongfeng
Large language models (LLMs) have shown remarkable performances across a wide range of tasks. However, the mechanisms by which these models encode tasks of varying complexities remain poorly understood. In this paper, we explore the hypothesis that L
Externí odkaz:
http://arxiv.org/abs/2404.07066
Autor:
Li, Yanda, Wang, Dixuan, Liang, Jiaqing, Jiang, Guochao, He, Qianyu, Xiao, Yanghua, Yang, Deqing
Large Language Models (LLMs) have demonstrated good performance in many reasoning tasks, but they still struggle with some complicated reasoning tasks including logical reasoning. One non-negligible reason for LLMs' suboptimal performance on logical
Externí odkaz:
http://arxiv.org/abs/2404.04293