TherML: Thermodynamics of Machine Learning. Alemi, A. A. & Fischer, I. Paper abstract bibtex 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.
@article{alemiTherMLThermodynamicsMachine2018,
archivePrefix = {arXiv},
eprinttype = {arxiv},
eprint = {1807.04162},
primaryClass = {cond-mat, stat},
title = {{{TherML}}: {{Thermodynamics}} of {{Machine Learning}}},
url = {http://arxiv.org/abs/1807.04162},
shorttitle = {{{TherML}}},
abstract = {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.},
urldate = {2019-05-22},
date = {2018-07-11},
keywords = {Statistics - Machine Learning,Condensed Matter - Statistical Mechanics,Computer Science - Machine Learning},
author = {Alemi, Alexander A. and Fischer, Ian},
file = {/home/dimitri/Nextcloud/Zotero/storage/XQJKMW9R/Alemi and Fischer - 2018 - TherML Thermodynamics of Machine Learning.pdf;/home/dimitri/Nextcloud/Zotero/storage/4B76X7GC/1807.html}
}
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