Information Bottleneck Theory and Applications in Deep Learning

• Other Authors: Geiger, Bernhard (Editor), Kubin, Gernot (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
• Subjects: Information technology industries; information theory; variational inference; machine learning; learnability; information bottleneck; representation learning; conspicuous subset; stochastic neural networks; mutual information; neural networks; information; bottleneck; compression; classification; optimization; classifier; decision tree; ensemble; deep neural networks; regularization methods; information bottleneck principle; deep networks; semi-supervised classification; latent space representation; hand crafted priors; learnable priors; regularization; deep learning
• Online Access: DOAB: download the publication; DOAB: description of the publication

The celebrated information bottleneck (IB) principle of Tishby et al. has recently enjoyed renewed attention due to its application in the area of deep learning. This collection investigates the IB principle in this new context. The individual chapters in this collection: • provide novel insights in...