Zobrazeno 1 - 10
of 1 214
pro vyhledávání: '"Zhang, Jiaojiao"'
Many machine learning tasks, such as principal component analysis and low-rank matrix completion, give rise to manifold optimization problems. Although there is a large body of work studying the design and analysis of algorithms for manifold optimiza
Externí odkaz:
http://arxiv.org/abs/2406.08465
We introduce a novel differentially private algorithm for online federated learning that employs temporally correlated noise to enhance utility while ensuring privacy of continuously released models. To address challenges posed by DP noise and local
Externí odkaz:
http://arxiv.org/abs/2403.16542
We propose a novel algorithm, termed soft quasi-Newton (soft QN), for optimization in the presence of bounded noise. Traditional quasi-Newton algorithms are vulnerable to such perturbations. To develop a more robust quasi-Newton method, we replace th
Externí odkaz:
http://arxiv.org/abs/2403.02448
Autor:
Qi, Yifei, Ni, Longqun, Ye, Zhenyu, Zhang, Jiaojiao, Bao, Xingyu, Wang, Pan, Rao, Yunjiang, Raposo, Ernesto P., Gomes, Anderson S. L., Wang, Zinan
Spin glass theory, as a paradigm for describing disordered magnetic systems, constitutes a prominent subject of study within statistical physics. Replica symmetry breaking (RSB), as one of the pivotal concepts for the understanding of spin glass theo
Externí odkaz:
http://arxiv.org/abs/2312.10898
Autor:
Bao, Xingyu, Lin, Shengtao, Zhang, Jiaojiao, Liang, Yongxin, Wan, Anchi, Qi, Yifei, Wang, Zinan
Er-doped random fiber laser (ERFL) is a complex physical system, and understanding its intrinsic physical mechanisms is crucial for promoting applications. In this paper, we experimentally investigate the time-domain statistical properties of ERFL un
Externí odkaz:
http://arxiv.org/abs/2312.05906
We propose a novel algorithm for solving the composite Federated Learning (FL) problem. This algorithm manages non-smooth regularization by strategically decoupling the proximal operator and communication, and addresses client drift without any assum
Externí odkaz:
http://arxiv.org/abs/2309.01795
This paper proposes a locally differentially private federated learning algorithm for strongly convex but possibly nonsmooth problems that protects the gradients of each worker against an honest but curious server. The proposed algorithm adds artific
Externí odkaz:
http://arxiv.org/abs/2308.01139
We consider the consensus problem in a decentralized network, focusing on a compact submanifold that acts as a nonconvex constraint set. By leveraging the proximal smoothness of the compact submanifold, which encompasses the local singleton property
Externí odkaz:
http://arxiv.org/abs/2306.04769
Autor:
Wang, Xiangtao, Wang, Ruizhi, Tian, Biao, Zhang, Jiaojiao, Zhang, Shuo, Chen, Junyang, Lukasiewicz, Thomas, Xu, Zhenghua
Existing self-supervised learning methods based on contrastive learning and masked image modeling have demonstrated impressive performances. However, current masked image modeling methods are mainly utilized in natural images, and their applications
Externí odkaz:
http://arxiv.org/abs/2302.13699
Autor:
Ren, Xingyu1 (AUTHOR) 18299520272@163.com, Zhang, Jiaojiao1 (AUTHOR), Dai, Anying1 (AUTHOR), Sun, Pengzhi1 (AUTHOR), Zhang, Yibo1 (AUTHOR), Jin, Lu1 (AUTHOR), Pan, Le1 (AUTHOR) chempan03@163.com
Publikováno v:
International Journal of Molecular Sciences. Sep2024, Vol. 25 Issue 17, p9634. 13p.