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Neural Networks, 2018
In this work, we introduce the graph regularized autoencoder. We propose three variants. The first one is the unsupervised version. The second one is tailored for clustering, by incorporating subspace clustering terms into the autoencoder formulation.
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In this work, we introduce the graph regularized autoencoder. We propose three variants. The first one is the unsupervised version. The second one is tailored for clustering, by incorporating subspace clustering terms into the autoencoder formulation.
openaire +2 more sources
Artificial Life
Abstract This letter presents the idea that neural backpropagation is exploiting dendritic processing to enable individual neurons to perform autoencoding. Using a very simple connection weight search heuristic and artificial neural network model, the effects of interleaving autoencoding for each neuron in a hidden layer of a feedforward
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Abstract This letter presents the idea that neural backpropagation is exploiting dendritic processing to enable individual neurons to perform autoencoding. Using a very simple connection weight search heuristic and artificial neural network model, the effects of interleaving autoencoding for each neuron in a hidden layer of a feedforward
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A comprehensive survey on design and application of autoencoder in deep learning
Applied Soft Computing Journal, 2023Yan Pei
exaly
Commonality Autoencoder: Learning Common Features for Change Detection From Heterogeneous Images
IEEE Transactions on Neural Networks and Learning Systems, 2022Yue Wu, Yongzhe Yuan, Maoguo Gong
exaly
Improved autoencoder for unsupervised anomaly detection
International Journal of Intelligent Systems, 2021Zhen Cheng, Siwei Wang, Pei Zhang
exaly
Deep fisher autoencoder combined with self-organizing map for visual industrial process monitoring
Journal of Manufacturing Systems, 2020Xuefeng Yan
exaly