TherML: The Thermodynamics of Machine Learning

Alexander A. Alemi, Ian Fischer

Sep 27, 2018 ICLR 2019 Conference Blind Submission readers: everyone Show Bibtex
  • Abstract: In this work we offer an information-theoretic framework for representation learning that connects with a wide class of existing objectives in machine learning. We develop a formal correspondence between this work and thermodynamics and discuss its implications.
  • Keywords: representation learning, information theory, information bottleneck, thermodynamics, predictive information
  • TL;DR: We offer a framework for representation learning that connects with a wide class of existing objectives and is analogous to thermodynamics.
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