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Nonredundant sparse feature extraction using autoencoders with receptive fields clustering.

, and . Neural Networks, (2017)

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Building Efficient ConvNets using Redundant Feature Pruning., and . CoRR, (2018)Diversity Regularized Adversarial Deep Learning., , and . AIAI, volume 559 of IFIP Advances in Information and Communication Technology, page 292-306. Springer, (2019)Energy Conservation in Wireless Sensor Networks Using Partly-Informed Sparse Autoencoder., and . IEEE Access, (2019)On Correlation of Features Extracted by Deep Neural Networks., , and . IJCNN, page 1-8. IEEE, (2019)Energy-Efficient Deployment of Relay Nodes in Wireless Sensor Networks Using Evolutionary Techniques., and . Int. J. Wirel. Inf. Networks, 25 (2): 157-172 (2018)Using resting state functional MRI to build a personalized autism diagnosis system., , , , , , , , , and 1 other author(s). ISBI, page 1381-1385. IEEE, (2018)Clustering of receptive fields in Autoencoders., and . IJCNN, page 1310-1317. IEEE, (2016)Regularizing Deep Neural Networks by Enhancing Diversity in Feature Extraction., , and . IEEE Trans. Neural Networks Learn. Syst., 30 (9): 2650-2661 (2019)Visualizing and Understanding Nonnegativity Constrained Sparse Autoencoder in Deep Learning., , and . ICAISC (1), volume 9692 of Lecture Notes in Computer Science, page 3-14. Springer, (2016)Deep Learning of Constrained Autoencoders for Enhanced Understanding of Data., and . IEEE Trans. Neural Networks Learn. Syst., 29 (9): 3969-3979 (2018)