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AI and Information Theory

highlighted by JP Bouchaud

Statistical Criticality arises in Most Informative Representations

We explore how properties frequently encountered in physics such as symmetry, locality, compositionality, and
polynomial log-probability translate into exceptionally simple neural networks

Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures