Complex-valued autoencoders

DOI10.1016/j.neunet.2012.04.011zbMath1258.68111arXiv1108.4135OpenAlexW2157665330WikidataQ42240356 ScholiaQ42240356MaRDI QIDQ1941595

Zhiqin Lu, Pierre Baldi

Publication date: 13 March 2013

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

Full work available at URL: https://arxiv.org/abs/1108.4135

zbMATH Keywords

EM algorithmprincipal component analysiscritical pointsdifferential geometryunsupervised learninglinear networkscomplex numberscomplex neural networksautoencodersdeep architectures

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20)

Related Items (5)

Global optimization issues in deep network regression: an overview ⋮ Learning in the machine: random backpropagation and the deep learning channel ⋮ Learning the Structural Vocabulary of a Network ⋮ Boolean autoencoders and hypercube clustering complexity ⋮ Piecewise convexity of artificial neural networks

Uses Software

• darch

Cites Work

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• Reducing the Dimensionality of Data with Neural Networks
• Deep Belief Networks Are Compact Universal Approximators
• Complex-Valued Neural Networks
• A Connection Between Score Matching and Denoising Autoencoders
• A Fast Learning Algorithm for Deep Belief Nets

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