Information Theoretic-Learning Auto-Encoder

22 Mar 2016Eder SantanaMatthew EmighJose C Principe

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... (read more)

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