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arXiv:1606.06737cond-mat.dis-nn

Criticality in Formal Languages and Statistical Physics

Henry W. Lin, Max Tegmark

Abstract

We show that the mutual information between two symbols, as a function of the number of symbols between the two, decays exponentially in any probabilistic regular grammar, but can decay like a power law for a context-free grammar. This result about formal languages is closely related to a well-known result in classical statistical mechanics that there are no phase transitions in dimensions fewer than two. It is also related to the emergence of power-law correlations in turbulence and cosmological inflation through recursive generative processes. We elucidate these physics connections and comment on potential applications of our results to machine learning tasks like training artificial recurrent neural networks. Along the way, we introduce a useful quantity which we dub the rational mutual information and discuss generalizations of our claims involving more complicated Bayesian networks.

arXiv comments
Replaced to match final published version. Discussion improved, references added
Journal reference
Entropy, 19, 299 (2017)
DOI
doi:10.3390/e19070299
Subjects
cond-mat.dis-nnDisordered Systems and Neural Networks cs.CLComputation and Language

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Linked by 2 identifiable commenters in 2 comments across 2 threads, from Sep 2016 to Oct 2016. Found in 1 HN submission from Jul 2016 to Jul 2016. Open submission discussions.

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    • Thanks for sharing this lecture by Max Tegmark. He comments on the video linking to two papers on arXiv which relate to the material in the lecture: https://arxiv.org/abs/1608.08225 and https://arxiv.org/abs/1606.06737

      throwaway000002View on HNParent context

    • According to a talk by Max Tegmark[0] (and its associated paper[1]), neural nets (particularly LSTMs) might be inherently better at this sort of thing due to the way they model mutual information. Markov models are best suited to situations where an observation k-steps in the…

      clickokView on HNParent context

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