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pro vyhledávání: '"Jun Yu"'
Automatic detection of depression is a rapidly growing field of research at the intersection of psychology and machine learning. However, with its exponential interest comes a growing concern for data privacy and scarcity due to the sensitivity of su
Externí odkaz:
http://arxiv.org/abs/2411.17672
Autor:
Chen, Jun Yu, Gao, Tao
We present APT, an advanced Large Language Model (LLM)-driven framework that enables autonomous agents to construct complex and creative structures within the Minecraft environment. Unlike previous approaches that primarily concentrate on skill-based
Externí odkaz:
http://arxiv.org/abs/2411.17255
Autor:
Wu, Sean, Chen, Jun Yu, Mohammadzadeh, Vahid, Besharati, Sajad, Lee, Jaewon, Nouri-Mahdavi, Kouros, Caprioli, Joseph, Fei, Zhe, Scalzo, Fabien
Perimetric measurements provide insight into a patient's peripheral vision and day-to-day functioning and are the main outcome measure for identifying progression of visual damage from glaucoma. However, visual field data can be noisy, exhibiting hig
Externí odkaz:
http://arxiv.org/abs/2411.12146
We present a pragmatic approach to lower down the mass scale of right-handed neutrinos in leptogenesis by introducing a scalar decaying to right-handed neutrinos. The key point of our proposal is that the out-of-equilibrium decays of the scalar provi
Externí odkaz:
http://arxiv.org/abs/2406.13468
Model editing is an emerging field that focuses on updating the knowledge embedded within large language models (LLMs) without extensive retraining. However, current model editing methods significantly compromise the general abilities of LLMs as the
Externí odkaz:
http://arxiv.org/abs/2405.16821
Autor:
Feng, Xiang, He, Yongbo, Wang, Yubo, Yang, Yan, Li, Wen, Chen, Yifei, Kuang, Zhenzhong, ding, Jiajun, Fan, Jianping, Jun, Yu
Recently, 3D Gaussian Splatting (3DGS) has gained popularity as a novel explicit 3D representation. This approach relies on the representation power of Gaussian primitives to provide a high-quality rendering. However, primitives optimized at low reso
Externí odkaz:
http://arxiv.org/abs/2404.10318
Dielectric metalens has emerged as an attractive device for advanced imaging system because of its powerful manipulation ability of light beam, small volume, and light weight. However, the applications of silicon nitride (Si3N4) metalens are limited
Externí odkaz:
http://arxiv.org/abs/2402.11559
The spiral phase contrast microscope can clearly distinguish the morphological information of the low contrast objects (i.e., biological samples) because of the isotropic edge-enhancement effect, while the bright field microscope can image the overal
Externí odkaz:
http://arxiv.org/abs/2402.11554
Despite their exceptional capabilities, large language models (LLMs) are prone to generating unintended text due to false or outdated knowledge. Given the resource-intensive nature of retraining LLMs, there has been a notable increase in the developm
Externí odkaz:
http://arxiv.org/abs/2401.17623
Autor:
Gu, Jia-Chen, Xu, Hao-Xiang, Ma, Jun-Yu, Lu, Pan, Ling, Zhen-Hua, Chang, Kai-Wei, Peng, Nanyun
Model editing is a technique that edits the large language models (LLMs) with updated knowledge to alleviate hallucinations without resource-intensive retraining. While current model editing methods can effectively modify a model's behavior within a
Externí odkaz:
http://arxiv.org/abs/2401.04700