Zobrazeno 1 - 10
of 1 839
pro vyhledávání: '"Liao Xiaofeng"'
Publikováno v:
IEEE Transactions on Signal and Information Processing over Networks, 2024
Distributed optimization is manifesting great potential in multiple fields, e.g., machine learning, control, and resource allocation. Existing decentralized optimization algorithms require sharing explicit state information among the agents, which ra
Externí odkaz:
http://arxiv.org/abs/2308.08164
Deep hiding, embedding images with others using deep neural networks, has demonstrated impressive efficacy in increasing the message capacity and robustness of secret sharing. In this paper, we challenge the robustness of existing deep hiding schemes
Externí odkaz:
http://arxiv.org/abs/2308.01512
Autor:
Jiang, Shan1,2,3 (AUTHOR), Liao, Xiaofeng2 (AUTHOR) xfliao@cqu.edu.cn, Feng, Yuming1,3 (AUTHOR) ymfeng@sanxiau.edu.cn, Gao, Zilin1 (AUTHOR), Onasanya, Babatunde Oluwaseun4 (AUTHOR)
Publikováno v:
PLoS ONE. 10/28/2024, Vol. 19 Issue 10, p1-21. 21p.
Publikováno v:
IEEE Transactions on Network Science and Engineering, 2024
We deal with a general distributed constrained online learning problem with privacy over time-varying networks, where a class of nondecomposable objectives are considered. Under this setting, each node only controls a part of the global decision, and
Externí odkaz:
http://arxiv.org/abs/2206.07944
This paper investigates two accelerated primal-dual mirror dynamical approaches for smooth and nonsmooth convex optimization problems with affine and closed, convex set constraints. In the smooth case, an accelerated primal-dual mirror dynamical appr
Externí odkaz:
http://arxiv.org/abs/2205.15983
Autor:
Li, Chengguo, Yu, Minhao, Liu, Weizhen, Zhang, Wei, Jiang, Weizhong, Zhang, Peng, Zeng, Xinyu, Di, Maojun, Liao, Xiaofeng, Zheng, Yongbin, Xiong, Zhiguo, Xia, Lijian, Sun, Yueming, Zhang, Rui, Zhong, Ming, Lin, Guole, Lin, Rong, Tao, Kaixiong
Publikováno v:
In Heliyon 15 April 2024 10(7)
Adversarial attacks have threatened the application of deep neural networks in security-sensitive scenarios. Most existing black-box attacks fool the target model by interacting with it many times and producing global perturbations. However, global p
Externí odkaz:
http://arxiv.org/abs/2101.01032
Publikováno v:
In Journal of the Franklin Institute January 2024 361(2):899-915
Publikováno v:
In Materials Science & Engineering B October 2023 296
Publikováno v:
In Neural Networks April 2023 161:693-707