Results 161 to 170 of about 75,818 (178)
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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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Deep Autoencoder Neural Networks: A Comprehensive Review and New Perspectives
Archives of Computational Methods in EngineeringIbomoiye Domor Mienye, Theo G. Swart
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Clustering Driven Deep Autoencoder for Video Anomaly Detection
European Conference on Computer Vision, 2020Yunpeng Chang +3 more
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