Zobrazeno 1 - 10
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pro vyhledávání: '"LIU Yuyang"'
Autor:
Liu Yuyang
Publikováno v:
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)
To improve social governance capacity, this paper proposes a social governance capacity evaluation system based on the ADDIE model. The system is mainly based on the public’s subjective evaluation of the effect of social governance, and 40 evaluati
Externí odkaz:
https://doaj.org/article/92855ae99a3140b5b5929bd15ff2e022
Publikováno v:
Jixie chuandong, Vol 46, Pp 106-111 (2022)
A lower limb exoskeleton rehabilitation robot can effectively improve the rehabilitation efficiency of patients with impaired lower limb motor function and reduce the workload of rehabilitation physicians. Therefore, a lower limb exoskeleton rehabili
Externí odkaz:
https://doaj.org/article/b1bde096fad84677a04f42c1ee29be85
Publikováno v:
Zhongliu Fangzhi Yanjiu, Vol 49, Iss 6, Pp 514-521 (2022)
The tumors of central nervous system refer to a group of benign and malignant diseases originating from tissues or structures within the central nervous system. Common tumors of central nervous system are sporadic, but a few have familial onset. Comp
Externí odkaz:
https://doaj.org/article/d60895a0c333430b8ff5c1a1bded5b0c
Autor:
Cao, Meng, Liu, Yuyang, Liu, Yingfei, Wang, Tiancai, Dong, Jiahua, Ding, Henghui, Zhang, Xiangyu, Reid, Ian, Liang, Xiaodan
Instruction tuning constitutes a prevalent technique for tailoring Large Vision Language Models (LVLMs) to meet individual task requirements. To date, most of the existing approaches are confined to single-task adaptation, whereas the requirements in
Externí odkaz:
http://arxiv.org/abs/2411.02564
Incremental object detection (IOD) is challenged by background shift, where background categories in sequential data may include previously learned or future classes. Inspired by the vision-language foundation models such as CLIP, these models captur
Externí odkaz:
http://arxiv.org/abs/2410.05804
Incremental semantic segmentation endeavors to segment newly encountered classes while maintaining knowledge of old classes. However, existing methods either 1) lack guidance from class-specific knowledge (i.e., old class prototypes), leading to a bi
Externí odkaz:
http://arxiv.org/abs/2407.09047
Autor:
Po, Lai-Man, Liu, Yuyang, Wu, Haoxuan, Zhang, Tianqi, Yu, Wing-Yin, Wang, Zhuohan, Jiang, Zeyu, Li, Kun
This paper introduces Standard Basis LoRA (SBoRA), a novel parameter-efficient fine-tuning approach for Large Language Models that builds upon the pioneering works of Low-Rank Adaptation (LoRA) and Orthogonal Adaptation. SBoRA reduces the number of t
Externí odkaz:
http://arxiv.org/abs/2407.05413
Autor:
Ershov, Egor, Panshin, Artyom, Karasev, Oleg, Korchagin, Sergey, Lev, Shepelev, Startsev, Alexandr, Vladimirov, Daniil, Zaychenkova, Ekaterina, Banić, Nikola, Iarchuk, Dmitrii, Efimova, Maria, Timofte, Radu, Terekhin, Arseniy, Yue, Shuwei, Liu, Yuyang, Wei, Minchen, Xu, Lu, Zhang, Chao, Wang, Yasi, Kınlı, Furkan, Yılmaz, Doğa, Özcan, Barış, Kıraç, Furkan, Liu, Shuai, Xiao, Jingyuan, Feng, Chaoyu, Wang, Hao, Shao, Guangqi, Zhang, Yuqian, Huang, Yibin, Luo, Wei, Wang, Liming, Wang, Xiaotao, Lei, Lei, Zini, Simone, Rota, Claudio, Buzzelli, Marco, Bianco, Simone, Schettini, Raimondo, Guo, Jin, Liu, Tianli, Wu, Mohao, Shao, Ben, Yang, Qirui, Li, Xianghui, Cheng, Qihua, Zhang, Fangpu, Xu, Zhiqiang, Yang, Jingyu, Yue, Huanjing
This paper presents a review of the NTIRE 2024 challenge on night photography rendering. The goal of the challenge was to find solutions that process raw camera images taken in nighttime conditions, and thereby produce a photo-quality output images i
Externí odkaz:
http://arxiv.org/abs/2406.13007
Autor:
Goswami, Dipam, Soutif--Cormerais, Albin, Liu, Yuyang, Kamath, Sandesh, Twardowski, Bartłomiej, van de Weijer, Joost
Continual learning methods are known to suffer from catastrophic forgetting, a phenomenon that is particularly hard to counter for methods that do not store exemplars of previous tasks. Therefore, to reduce potential drift in the feature extractor, e
Externí odkaz:
http://arxiv.org/abs/2405.19074
Autor:
Lv, Liuzhenghao, Lin, Zongying, Li, Hao, Liu, Yuyang, Cui, Jiaxi, Chen, Calvin Yu-Chian, Yuan, Li, Tian, Yonghong
Large Language Models (LLMs) have achieved remarkable performance in multiple Natural Language Processing (NLP) tasks. Under the premise that protein sequences constitute the protein language, Protein Language Models(PLMs) have advanced the field of
Externí odkaz:
http://arxiv.org/abs/2402.16445