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research article

The physics of energy-based models

Huembeli, Patrick  
•
Arrazola, Juan Miguel
•
Killoran, Nathan
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June 1, 2022
Quantum Machine Intelligence

Energy-based models (EBMs) are experiencing a resurgence of interest in both the physics community and the machine learning community. This article provides an intuitive introduction to EBMs, without requiring any background in machine learning, connecting elementary concepts from physics with basic concepts and tools in generative models, and finally giving a perspective where current research in the field is heading. This article, in its original form, was written as an online lecture note in HTML and Javascript and contains interactive graphics. We recommend the reader to also visit the interactive version.

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Type
research article
DOI
10.1007/s42484-021-00057-7
Web of Science ID

WOS:000739948300001

Author(s)
Huembeli, Patrick  
Arrazola, Juan Miguel
Killoran, Nathan
Mohseni, Masoud
Wittek, Peter
Date Issued

2022-06-01

Published in
Quantum Machine Intelligence
Volume

4

Issue

1

Article Number

1(2022)

Subjects

Computer Science, Artificial Intelligence

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Quantum Science & Technology

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Computer Science

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Physics

•

energy-based models

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machine learning

•

spin glasses

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gibbs sampling

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restricted boltzmann machines

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spin-glass models

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quantum

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optimization

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algorithm

•

networks

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
CQSL  
Available on Infoscience
January 31, 2022
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/185026
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