Efficient learning rate adaptation based on hierarchical optimization approach

DOI10.1016/j.neunet.2022.02.014OpenAlexW4214562615WikidataQ114950227 ScholiaQ114950227MaRDI QIDQ6072584

Gyoung S. Na

Publication date: 13 October 2023

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2022.02.014

zbMATH Keywords

computer visionmathematical optimizationdeep learning

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Applications of mathematical programming (90C90) Machine vision and scene understanding (68T45)

Cites Work

• Unnamed Item
• A Stochastic Quasi-Newton Method for Large-Scale Optimization
• Noisy matrix decomposition via convex relaxation: optimal rates in high dimensions
• A dual algorithm for the solution of nonlinear variational problems via finite element approximation
• Global convergence of unmodified 3-block ADMM for a class of convex minimization problems
• Multiplier and gradient methods
• Linear Inversion of Band-Limited Reflection Seismograms
• The direct extension of ADMM for multi-block convex minimization problems is not necessarily convergent

This page was built for publication: Efficient learning rate adaptation based on hierarchical optimization approach