Training threshold neural networks by extreme learning machine and adaptive stochastic resonance

DOI10.1016/J.PHYSLETA.2022.128008OpenAlexW4283364262WikidataQ114949328 ScholiaQ114949328MaRDI QIDQ2079079

Zejia Chen, Fabing Duan, François Chapeau-Blondeau, Derek Abbott

Publication date: 4 March 2022

Published in: Physics Letters. A (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.physleta.2022.128008

zbMATH Keywords

classificationfunction approximationextreme learning machinenoise injectionthreshold networkadaptive stochastic resonance

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• UCI-ml
• XNOR-Net

Cites Work

• Stochastic resonance in hybrid scale-free neuronal networks
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• Emergence of stochastic resonance in a two-compartment hippocampal pyramidal neuron model
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• Stochastic Resonance
• Nonparametric One-Bit Quantizers for Distributed Estimation
• Bayes Risk Reduction of Estimators Using Artificial Observation Noise
• Gradient‐based parameter optimization for systems containing discrete‐valued functions
• The Stochastic Delta Rule: Faster and More Accurate Deep Learning Through Adaptive Weight Noise
• MMSE Approximation For Sparse Coding Algorithms Using Stochastic Resonance
• A logical calculus of the ideas immanent in nervous activity

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