Computer Science > Machine Learning
arXiv:2409.06542 (cs)
[Submitted on 10 Sep 2024]
Title:Dynamic Decoupling of Placid Terminal Attractor-based Gradient Descent Algorithm
Authors:Jinwei Zhao (1),Marco Gori (2),Alessandro Betti (3),Stefano Melacci (2),Hongtao Zhang (1),Jiedong Liu (1),Xinhong Hei (1) ((1) Faculty of Computer Science and Engineering, Xi'an University of Technology, Xi'an, China (2) Department of Information Engineering and Mathematics, University of Siena, Siena, Italy (3) IMT Scuola Alti Studi, Lucca, Italy)
View a PDF of the paper titled Dynamic Decoupling of Placid Terminal Attractor-based Gradient Descent Algorithm, by Jinwei Zhao (1) and 13 other authors
View PDFHTML (experimental)Abstract:Gradient descent (GD) and stochastic gradient descent (SGD) have been widely used in a large number of application domains. Therefore, understanding the dynamics of GD and improving its convergence speed is still of great importance. This paper carefully analyzes the dynamics of GD based on the terminal attractor at different stages of its gradient flow. On the basis of the terminal sliding mode theory and the terminal attractor theory, four adaptive learning rates are designed. Their performances are investigated in light of a detailed theoretical investigation, and the running times of the learning procedures are evaluated and compared. The total times of their learning processes are also studied in detail. To evaluate their effectiveness, various simulation results are investigated on a function approximation problem and an image classification problem.
| Comments: | 8 pages, 4 figures |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2409.06542 [cs.LG] |
| (orarXiv:2409.06542v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2409.06542 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Dynamic Decoupling of Placid Terminal Attractor-based Gradient Descent Algorithm, by Jinwei Zhao (1) and 13 other authors
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