Results 121 to 130 of about 27,500 (171)

A Novel Low-Rank Embedded Latent Multi-View Subspace Clustering Approach. [PDF]

Sensors (Basel)
Wang S, Chen L, Liang Z, Liu Q.
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Directivity Maximization of Difference Patterns for Monopulse Microstrip Patch Arrays with Sidelobe Constraints. [PDF]

Micromachines (Basel)
Li W, Jiao YC, Zhang Y, Zhang L.
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Multi-Frequency GPR Image Fusion Based on Convolutional Sparse Representation to Enhance Road Detection. [PDF]

J Imaging
Fang L, Yang F, Fang Y, Nie J.
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MRDagent: iterative and adaptive parameter optimization for stable ctDNA-based MRD detection in heterogeneous samples. [PDF]

Bioinformatics
Wang T, Lai X, Wang S, Liu Y, Zhu X, Wang J.   +5 more
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Federated spatial-temporal traffic forecasting with VMD-enhanced graph attention and LSTM. [PDF]

Sci Rep
Mundada T, Ramdhave S, Jain S, Gupta S.
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Intelligent cybersecurity management in industrial IoT system using attribute reduction with collaborative deep learning enabled false data injection attack detection approach. [PDF]

Sci Rep
Alrashdi I, Duraibi S.
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Entanglement-enhanced quantum lock-in detection achieving Heisenberg scaling. [PDF]

Nat Commun
Zhang JW, Zhuang M, Wang B, Yuan WF, Li JC, Ding GY, Ding WQ, Chen L, Chen SJ, Zhou F, Lee C, Feng M.   +11 more
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Exact feature collisions in neural networks. [PDF]

Sci Rep
Ozbulak U, Rao S, De Neve W, Vankerschaver J, Van Messem A, Gasparyan M.   +5 more
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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
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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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