Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/136059
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Type: | Journal article |
Title: | Generalization of stochastic-resonance-based threshold networks with Tikhonov regularization |
Author: | Bai, S. Duan, F. Chapeau-Blondeau, F. Abbott, D. |
Citation: | Physical Review E, 2022; 106(1):L012101-1-L012101-5 |
Publisher: | American Physical Society |
Issue Date: | 2022 |
ISSN: | 2470-0045 2470-0053 |
Statement of Responsibility: | Saiya Bai, Fabing Duan, François Chapeau-Blondeau, and Derek Abbott |
Abstract: | Injecting artificial noise into a feedforward threshold neural network allows it to become trainable by gradientbased methods and also enlarges the parameter space as well as the range of synaptic weights. This configuration constitutes a stochastic-resonance-based threshold neural network, where the noise level can adaptively converge to a nonzero optimal value for finding a local minimum of the loss criterion. We prove theoretically that the injected noise plays the role of a generalized Tikhonov regularizer for training the designed threshold network. Experiments on regression and classification problems demonstrate that the generalization of the stochasticresonance- based threshold network is improved by the injection of noise. The feasibility of injecting noise into the threshold neural network opens up the potential for adaptive stochastic resonance in machine learning. |
Rights: | ©2022 American Physical Society. |
DOI: | 10.1103/PhysRevE.106.L012101 |
Grant ID: | http://purl.org/au-research/grants/arc/DP200103795 |
Published version: | http://dx.doi.org/10.1103/physreve.106.l012101 |
Appears in Collections: | Physics publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.