Zobrazeno 1 - 10
of 535
pro vyhledávání: '"Lu, Jiangang"'
Empirical Risk Minimization (ERM) is fragile in scenarios with insufficient labeled samples. A vanilla extension of ERM to unlabeled samples is Entropy Minimization (EntMin), which employs the soft-labels of unlabeled samples to guide their learning.
Externí odkaz:
http://arxiv.org/abs/2406.02862
Recent advances achieved by deep learning models rely on the independent and identically distributed assumption, hindering their applications in real-world scenarios with domain shifts. To tackle this issue, cross-domain learning aims at extracting d
Externí odkaz:
http://arxiv.org/abs/2401.03253
Limited transferability hinders the performance of deep learning models when applied to new application scenarios. Recently, Unsupervised Domain Adaptation (UDA) has achieved significant progress in addressing this issue via learning domain-invariant
Externí odkaz:
http://arxiv.org/abs/2309.14360
Limited transferability hinders the performance of deep learning models when applied to new application scenarios. Recently, unsupervised domain adaptation (UDA) has achieved significant progress in addressing this issue via learning domain-invariant
Externí odkaz:
http://arxiv.org/abs/2303.12724
Publikováno v:
Frontiers in Energy Research, Vol 12 (2024)
With the advancement of source-load interaction in the new power systems, data-driven approaches have provided a foundational support for aggregating and interacting between sources and loads. However, with the widespread integration of distributed e
Externí odkaz:
https://doaj.org/article/1cb3f22487c24a2babc81f3f51e1bb56
Publikováno v:
In Electric Power Systems Research October 2024 235
Autor:
Lu, Jiangang, Zhao, Ruifeng, Hou, Zufeng, Lin, Guihui, Zhang, Yong, Wang, Chao, Pan, Kaiyan, Liu, Haixin
Publikováno v:
In Electric Power Systems Research February 2025 239
Autor:
Zhang, Qing, He, Yi, Zhang, Lifeng, Lu, Jiangang, Gao, Binghai, Yang, Wang, Chen, Hesheng, Zhang, Yalei
Publikováno v:
In Gondwana Research August 2024 132:323-342
Publikováno v:
In Applied Surface Science 15 March 2024 649
Adversarial attacks on graphs have attracted considerable research interests. Existing works assume the attacker is either (partly) aware of the victim model, or able to send queries to it. These assumptions are, however, unrealistic. To bridge the g
Externí odkaz:
http://arxiv.org/abs/2012.06757