An efficient pruning and fine-tuning method for deep spiking neural network-Reference-Cited by-同舟云学术

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An efficient pruning and fine-tuning method for deep spiking neural network

Published:2023-10-19 Issue:23 Volume:53 Page:28910-28923
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Meng L. W.,Qiao G. C.,Zhang X. Y.,Bai J.,Zuo Y.,Zhou P. J.,Liu Y.,Hu S. G.

Funder

STI 2030-Major Projects

Open Foundation of State Key Laboratory of Electronic Thin Films and Integrated Devices

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-023-05056-8.pdf

Reference51 articles.

1. Samek W, Montavon G, Lapuschkin S, Anders CJ, Mueller K-R (2021) Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications. Proc Ieee, Rev 109(3):247–278. https://doi.org/10.1109/jproc.2021.3060483

2. Otter DW, Medina JR, Kalita JK (2021) A Survey of the Usages of Deep Learning for Natural Language Processing. Ieee Trans Neural Networks Learning Syst 32(2):604–624. https://doi.org/10.1109/tnnls.2020.2979670

3. Liu C et al (2022) A programmable diffractive deep neural network based on a digital-coding metasurface array. Nat Electron 5(2):113–122. https://doi.org/10.1038/s41928-022-00719-9

4. Sun Y, Xu J, Lin G, Ji W, Wang L (2022) RBF Neural Network-Based Supervisor Control for Maglev Vehicles on an Elastic Track With Network Time Delay. Ieee Trans Industrial Inform 18(1):509–519. https://doi.org/10.1109/tii.2020.3032235

5. Fei J, Liu L (2022) Real-Time Nonlinear Model Predictive Control of Active Power Filter Using Self-Feedback Recurrent Fuzzy Neural Network Estimator. Ieee Trans Industrial Electron 69(8):8366–8376. https://doi.org/10.1109/tie.2021.3106007

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1. Learning improvement of spiking neural networks with dynamic adaptive hyperparameter neurons;Applied Intelligence;2024-07-10

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