Entropy, Volume 21, Issue 12 (December 2019) – 105 articles
Cover Story (view full-size image): The information bottleneck (IB) method is a technique for extracting information in one random variable that is relevant for predicting another random variable. IB has applications in many fields, including machine learning with neural networks. In order to perform IB, however, one must find an optimally-compressed "bottleneck" random variable, which involves solving a difficult optimization problem with an information-theoretic objective function. We propose a method for solving this optimization problem using neural networks and a recently proposed bound on mutual information. We demonstrate that our approach exhibits better performance than other recent proposals. View this paper
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