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TherML: Thermodynamics of Machine Learning

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arxiv 1807.04162 v3 pith:3SQC33MU submitted 2018-07-11 cs.LG cond-mat.stat-mechstat.ML

Alexander A. Alemi , Ian Fischer This is my paper · ORCID
classification cs.LGcond-mat.stat-mechstat.ML
keywords learningmachinethermodynamicsworkclasscorrespondencedevelopdiscuss
verification ladder T0 review T1 audit T2 compute T3 formal
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In this work we offer a framework for reasoning about a wide class of existing objectives in machine learning. We develop a formal correspondence between this work and thermodynamics and discuss its implications.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. First-Passage Approach to Optimizing Perturbations for Improved Training of Machine Learning Models

    cs.LG 2025-02 conditional novelty 6.0 of 10

    The authors show that when unperturbed neural network training reaches a quasi-steady state, the mean time to a target test accuracy under periodic perturbations can be predicted from a single perturbation experiment.

  2. SGD as Free Energy Minimization: A Thermodynamic View on Neural Network Training

    cs.LG 2025-05 reject novelty 5.0 of 10

    SGD is said to minimize free energy, but the temperature is constructed from the data, making the validation circular.

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