Information Theoretic-Learning Auto-Encoder

March 22, 2016 · Declared Dead · 🏛 IEEE International Joint Conference on Neural Network

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Authors Eder Santana, Matthew Emigh, Jose C Principe arXiv ID 1603.06653 Category cs.LG: Machine Learning Citations 20 Venue IEEE International Joint Conference on Neural Network Last Checked 2 months ago
Abstract
We propose Information Theoretic-Learning (ITL) divergence measures for variational regularization of neural networks. We also explore ITL-regularized autoencoders as an alternative to variational autoencoding bayes, adversarial autoencoders and generative adversarial networks for randomly generating sample data without explicitly defining a partition function. This paper also formalizes, generative moment matching networks under the ITL framework.
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