Results 221 to 230 of about 27,483 (269)

Energy-Efficient Hierarchical Federated Learning in UAV Networks with Partial AI Model Upload Under Non-Convex Loss. [PDF]

Sensors (Basel)
Li H, Wang S, Du Y, Li R, Fan X, Luo C.
europepmc   +1 more source

ACMTF-R: Supervised multi-omics data integration uncovering shared and distinct outcome-associated variation. [PDF]

PLoS One
van der Ploeg GR, White FTG, Jakobsen RR, Westerhuis JA, Heintz-Buschart A, Smilde AK.   +5 more
europepmc   +1 more source

Smooth and shape-constrained quantile distributed lag models. [PDF]

Biometrics
Jin Y, Molstad AJ, Wilson A, Antonelli J.   +3 more
europepmc   +1 more source

Plug-and-Play Self-Supervised Denoising for Pulmonary Perfusion MRI. [PDF]

Bioengineering (Basel)
Sun C, Wang Y, Thornburgh C, Lin AL, Qing K, Mugler JP, Altes TA.   +6 more
europepmc   +1 more source

Identifying potential miRNA-disease associations through an accurate matrix completion approach. [PDF]

Brief Bioinform
Li B, Zhong K, Deveci M, Tang Y.
europepmc   +1 more source

Multi-modal transport of intensity diffraction tomography microscopy with an electrically tunable lens [Invited]. [PDF]

Biomed Opt Express
Zhou Z, Zhang R, Zhou N, Chen Q, Zuo C.
europepmc   +1 more source

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Bi-alternating direction method of multipliers

2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013
The alternating-direction method of multipliers (ADMM) has been widely applied in the field of distributed optimization and statistic learning. ADMM iteratively approaches the saddle point of an augmented Lagrangian function by performing three updates per-iteration.
Guoqiang Zhang, Richard Heusdens
openaire   +2 more sources

Accelerated Alternating Direction Method of Multipliers

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015
Recent years have seen a revival of interest in the Alternating Direction Method of Multipliers (ADMM), due to its simplicity, versatility, and scalability. As a first order method for general convex problems, the rate of convergence of ADMM is O(1=k) [4, 25].
Mojtaba Kadkhodaie, Konstantina Christakopoulou, Maziar Sanjabi, Arindam Banerjee   +3 more
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Alternating Direction Method of Multipliers for Linear Programming

Journal of the Operations Research Society of China, 2016
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
He, Bing Sheng, Yuan, Xiao Ming
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DLM: Decentralized Linearized Alternating Direction Method of Multipliers

IEEE Transactions on Signal Processing, 2015
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ling, Qing, Shi, Wei, Wu, Gang, Ribeiro, Alejandro   +3 more
openaire   +2 more sources