The enduring relevance of simple machine learning models

Hopfield’s associative memory model and Hinton’s Boltzmann machines showcase the importance of simplicity and interpretability in AI. Their work urges modern AI to balance power with transparency, ensuring models remain comprehensible for research, education, and broader applications.

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Publication Details

Journal
Europhysics news
Published
2025-01-01
DOI
https://doi.org/10.1051/epn/2025108
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
0.00
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article

The enduring relevance of simple machine learning models

Marco Baiesi, Samir Suweis
Europhysics news
Neural Networks and Applications
article

The enduring relevance of simple machine learning models

Marco Baiesi, Samir Suweis
article en

Abstract

Hopfield’s associative memory model and Hinton’s Boltzmann machines showcase the importance of simplicity and interpretability in AI. Their work urges modern AI to balance power with transparency, ensuring models remain comprehensible for research, education, and broader applications.

Europhysics newsVol. 56(1)
University of Padua (IT)
Openalex Percentile: Top 98%
Neural Networks and Applications
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The enduring relevance of simple machine learning models — Marco Baiesi, Samir Suweis · Europhysics news (2025) | TGRS Research Map | TGRS