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Pseudo-potentiality maximization for improved interpretation and generalization in neural networks.

. IWCIA, page 21-28. IEEE, (2015)

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Forced information and information loss in information-theoretic competitive learning.. Artificial Intelligence and Applications, page 78-84. IASTED/ACTA Press, (2007)Pseudo-potentiality maximization for improved interpretation and generalization in neural networks.. IWCIA, page 21-28. IEEE, (2015)Simplified Information Acquisition Method to Improve Prediction Performance: Direct Use of Hidden Neuron Outputs and Separation of Information Acquisition and Use Phase.. ANNIIP, page 78-87. SciTePress, (2014)Local Selective Learning for Interpreting Multi-Layered Neural Networks., , and . SCIS&ISIS, page 115-122. IEEE, (2018)Mutual information maximization by free energy-based competitive learning for self-organizing maps.. SMC, page 1819-1825. IEEE, (2008)Self-Organized Mutual Information Maximization Learning for Improved Generalization Performance.. SMC, page 1613-1618. IEEE, (2015)Controlling Excessive Information by Surface Information Criterion for Information-Theoretic Self-Organizing Maps.. SMC, page 5130-5134. IEEE, (2006)Competitive Learning by Information Maximization: Eliminating Dead Neurons in Competitive Learning.. ICANN, volume 2714 of Lecture Notes in Computer Science, page 99-106. Springer, (2003)Similarity interaction in information-theoretic self-organizing maps.. Int. J. General Systems, 42 (3): 239-267 (2013)Feature detectors by autoencoders: Decomposition of input patterns into atomic features by neural networks., and . Neural Processing Letters, 2 (6): 17-22 (1995)