On the importance of hidden bias and hidden entropy in representational efficiency of the Gaussian-bipolar restricted Boltzmann machines

DOI10.1016/j.neunet.2018.06.002zbMath1434.68509OpenAlexW2809651358WikidataQ89236516 ScholiaQ89236516MaRDI QIDQ2181099

Engin Erzin, Altynbek Isabekov

Publication date: 18 May 2020

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

Full work available at URL: http://cdm21054.contentdm.oclc.org/cdm/ref/collection/IR/id/8389

zbMATH Keywords

autoencoderdeep learninghidden biashidden entropyRBMrepresentational efficiency

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Computational aspects of data analysis and big data (68T09)

Cites Work

• Unnamed Item
• Reducing the Dimensionality of Data with Neural Networks
• Training Products of Experts by Minimizing Contrastive Divergence
• Representational Power of Restricted Boltzmann Machines and Deep Belief Networks

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