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pro vyhledávání: '"Zhang, Hanping"'
In recent years, semi-supervised learning (SSL) has gained significant attention due to its ability to leverage both labeled and unlabeled data to improve model performance, especially when labeled data is scarce. However, most current SSL methods re
Externí odkaz:
http://arxiv.org/abs/2405.01760
In this paper, we present a novel approach termed Prompt-Driven Feature Diffusion (PDFD) within a semi-supervised learning framework for Open World Semi-Supervised Learning (OW-SSL). At its core, PDFD deploys an efficient feature-level diffusion mode
Externí odkaz:
http://arxiv.org/abs/2404.11795
Autor:
Zhang, Hanping, Guo, Yuhong
As safety violations can lead to severe consequences in real-world robotic applications, the increasing deployment of Reinforcement Learning (RL) in robotic domains has propelled the study of safe exploration for reinforcement learning (safe RL). In
Externí odkaz:
http://arxiv.org/abs/2209.09648
Autor:
He, Zhipeng, Zhang, Jingjing, Guo, Xiumei, Kang, Hai, Wang, Zhihua, Liu, Yilin, Zhang, Hanping
Publikováno v:
In Materials & Design June 2024 242
Autor:
Zhang, Hanping, Guo, Yuhong
The generalization gap in reinforcement learning (RL) has been a significant obstacle that prevents the RL agent from learning general skills and adapting to varying environments. Increasing the generalization capacity of the RL systems can significa
Externí odkaz:
http://arxiv.org/abs/2106.15587
Publikováno v:
In Journal of Advanced Research May 2024
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Akademický článek
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To gain a better performance, many researchers put more computing resource into an application. However, in the AI area, there is still a lack of a successful large-scale machine learning training application: The scalability and performance reproduc
Externí odkaz:
http://arxiv.org/abs/1910.11510
Autor:
Shen, Daozhen, Chen, Xiaojuan, Chen, Chen, Yang, Baozhu, Jiang, Qingyan, Su, Lixin, Zhang, Hanping, Liu, Hong-Jiang, Liu, Qi
Publikováno v:
In Journal of Energy Storage 1 August 2023 64