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.

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2025-03-24
DOI
https://doi.org/10.1051/epn/2025108/pdf
Primary Topic
Generative Adversarial Networks and Image Synthesis
Type
article
Field-Weighted Citation Impact
0.00
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article

The enduring relevance of simple machine learning models

Springer Link (Chiba Institute of Technology)
Generative Adversarial Networks and Image Synthesis
article

The enduring relevance of simple machine learning models

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.

Springer Link (Chiba Institute of Technology)
Openalex Percentile: Top 57%
Generative Adversarial Networks and Image Synthesis
DOISourceOpenAlex
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The enduring relevance of simple machine learning models · Springer Link (Chiba Institute of Technology) (2025) | TGRS Research Map | TGRS