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.
The enduring relevance of simple machine learning models
Baiesi M.;Suweis S.
2025
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.File in questo prodotto:
| File | Dimensione | Formato | |
|---|---|---|---|
|
P___2501EPN_BaiesiSuweis_enduring-relevance.pdf
Accesso riservato
Tipologia:
Published (Publisher's Version of Record)
Licenza:
Accesso privato - non pubblico
Dimensione
1.83 MB
Formato
Adobe PDF
|
1.83 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.




