Zobrazeno 1 - 10
of 282
pro vyhledávání: '"Li, Feifan"'
Deep reinforcement learning has advanced greatly and applied in many areas. In this paper, we explore the vulnerability of deep reinforcement learning by proposing a novel generative model for creating effective adversarial examples to attack the age
Externí odkaz:
http://arxiv.org/abs/2312.12904
Autor:
Li, Feifan1 (AUTHOR) 2211170007@nbu.edu.cn, Dai, Zhuoheng1 (AUTHOR), Jiang, Lei1 (AUTHOR), Song, Chanfei1 (AUTHOR), Zhong, Caiming1 (AUTHOR) zhongcaiming@nbu.edu.cn, Chen, Yingna1 (AUTHOR) zhongcaiming@nbu.edu.cn
Publikováno v:
Sensors (14248220). Nov2024, Vol. 24 Issue 21, p6906. 26p.
Autor:
Mohamed, Youssef, Abdelfattah, Mohamed, Alhuwaider, Shyma, Li, Feifan, Zhang, Xiangliang, Church, Kenneth Ward, Elhoseiny, Mohamed
This paper introduces ArtELingo, a new benchmark and dataset, designed to encourage work on diversity across languages and cultures. Following ArtEmis, a collection of 80k artworks from WikiArt with 0.45M emotion labels and English-only captions, Art
Externí odkaz:
http://arxiv.org/abs/2211.10780
User engagement prediction plays a critical role for designing interaction strategies to grow user engagement and increase revenue in online social platforms. Through the in-depth analysis of the real-world data from the world's largest professional
Externí odkaz:
http://arxiv.org/abs/2210.12402
Recently, deep learning methods have made great progress in traffic prediction, but their performance depends on a large amount of historical data. In reality, we may face the data scarcity issue. In this case, deep learning models fail to obtain sat
Externí odkaz:
http://arxiv.org/abs/2207.01301
Publikováno v:
In Chemical Engineering Journal 15 November 2024 500
Publikováno v:
In Journal of Cleaner Production 1 November 2024 478
Publikováno v:
In Journal of Solid State Chemistry January 2025 341
Publikováno v:
In Energy Policy December 2024 195
Publikováno v:
In Journal of Clinical Virology Plus August 2024 4(3)