Neural architecture search applying optimal stopping theory

Neural architecture search (NAS) exploration requires tremendous amounts of computational power to properly explore. This makes exploration of modern NAS search spaces impractical for researchers due to the infrastructure investments required and the time needed to effectively design, train, validat...

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Bibliographic Details
Published in:Frontiers in Artificial Intelligence
Main Authors: Matthew Sheehan, Oleg Yakimenko
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-09-01
Subjects:
neural architecture search
Markov decision processes
automated machine learning
optimal stopping theory
secretary problem
Markov time
Online Access:https://www.frontiersin.org/articles/10.3389/frai.2025.1643088/full

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https://www.frontiersin.org/articles/10.3389/frai.2025.1643088/full

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