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pro vyhledávání: '"Liu, Yiyang"'
Autor:
Wang, Taowen, Liu, Yiyang, Liang, James Chenhao, zhao, junhan, Cui, Yiming, Mao, Yuning, Nie, Shaoliang, Liu, Jiahao, Feng, Fuli, Xu, Zenglin, Han, Cheng, Huang, Lifu, Wang, Qifan, Liu, Dongfang
Multimodal Large Language Models (MLLMs) demonstrate remarkable performance across a wide range of domains, with increasing emphasis on enhancing their zero-shot generalization capabilities for unseen tasks across various modalities. Instruction tuni
Externí odkaz:
http://arxiv.org/abs/2409.15657
In the facial expression recognition task, researchers always get low accuracy of expression classification due to a small amount of training samples. In order to solve this kind of problem, we proposes a new data augmentation method named MixCut. In
Externí odkaz:
http://arxiv.org/abs/2405.10489
Autor:
Ren, Qingyang, Jiang, Zilin, Cao, Jinghan, Li, Sijia, Li, Chiqu, Liu, Yiyang, Huo, Shuning, He, Tiange, Chen, Yuan
This survey explores the fairness of large language models (LLMs) in e-commerce, examining their progress, applications, and the challenges they face. LLMs have become pivotal in the e-commerce domain, offering innovative solutions and enhancing cust
Externí odkaz:
http://arxiv.org/abs/2405.13025
Autor:
Liu, Kaibo, Liu, Yiyang, Chen, Zhenpeng, Zhang, Jie M., Han, Yudong, Ma, Yun, Li, Ge, Huang, Gang
Conventional automated test generation tools struggle to generate test oracles and tricky bug-revealing test inputs. Large Language Models (LLMs) can be prompted to produce test inputs and oracles for a program directly, but the precision of the test
Externí odkaz:
http://arxiv.org/abs/2404.10304
Autor:
Zhang, Qiangbo, Yu, Zeqing, Wang, Mengguang, Liu, Yiyang, Zhang, Changwei, Wang, Chang, Zheng, Zhenrong
Single metalenses are limited by their physical constraints, precluding themselves from achieving high numerical aperture across a wide visible spectral band in large-aperture applications. A hybrid system that integrates a metalens with a refractive
Externí odkaz:
http://arxiv.org/abs/2404.03173
The inverse wave scattering problem seeks to estimate a heterogeneous, inaccessible medium, modeled by unknown variable coefficients in wave equations, from transient recordings of waves generated by probing signals. It is a widely studied inverse pr
Externí odkaz:
http://arxiv.org/abs/2403.03844
Knowledge Distillation (KD) transfers the knowledge from a high-capacity teacher model to promote a smaller student model. Existing efforts guide the distillation by matching their prediction logits, feature embedding, etc., while leaving how to effi
Externí odkaz:
http://arxiv.org/abs/2211.17059
Many recent named entity recognition (NER) studies criticize flat NER for its non-overlapping assumption, and switch to investigating nested NER. However, existing nested NER models heavily rely on training data annotated with nested entities, while
Externí odkaz:
http://arxiv.org/abs/2211.00301
Autor:
Liu, Yiyang1 (AUTHOR) yiyangliu0220@163.com, Li, Dongyang2 (AUTHOR) lidongyang0713@163.com, Liu, Yue3 (AUTHOR) yueliu@iae.ac.cn, Wang, Jiazheng4 (AUTHOR) wangjiazheng0918@163.com, Liu, Chang2 (AUTHOR) yueliu@iae.ac.cn
Publikováno v:
International Journal of Molecular Sciences. Aug2024, Vol. 25 Issue 15, p8546. 15p.
Span-based models are one of the most straightforward methods for named entity recognition (NER). Existing span-based NER systems shallowly aggregate the token representations to span representations. However, this typically results in significant in
Externí odkaz:
http://arxiv.org/abs/2210.04182