Towards automatically-tuned deep neural networks

Abstract: Recent advances in AutoML have led to automated tools that can compete with machine learning experts on supervised learning tasks. In this work, we present two versions of Auto-Net, which provide automatically-tuned deep neural networks without any human intervention. The first version, Auto-Net 1.0, builds upon ideas from the competition-winning system Auto-sklearn by using the Bayesian Optimization method SMAC and uses Lasagne as the underlying deep learning (DL) library. The more recent Auto-Net 2.0 builds upon a recent combination of Bayesian Optimization and HyperBand, called BOHB, and uses PyTorch as DL library. To the best of our knowledge, Auto-Net 1.0 was the first automatically-tuned neural network to win competition datasets against human experts (as part of the first AutoML challenge). Further empirical results show that ensembling Auto-Net 1.0 with Auto-sklearn can perform better than either approach alone, and that Auto-Net 2.0 can perform better yet

Standort
Deutsche Nationalbibliothek Frankfurt am Main
Umfang
Online-Ressource
Sprache
Englisch
Anmerkungen
Automated Machine Learning. - Cham : Hutter, Frank; Kotthoff, Lars; Vanschoren, Joaquin [Hrsg.], 2019. - 135-149, ISBN: 978-3-030-05318-5

Ereignis
Veröffentlichung
(wo)
Freiburg
(wer)
Universität
(wann)
2024
Urheber
Mendoza, Hector
Klein, Aaron
Feurer, Matthias
Springenberg, Jost Tobias
Urban, Matthias
Burkart, Michael
Dippel, Maximilian
Lindauer, Marius
Hutter, Frank
Beteiligte Personen und Organisationen
Maschinelles Lernen und Natürlichsprachliche Systeme, Professur Frank Hutter

DOI
10.1007/978-3-030-05318-5_7
URN
urn:nbn:de:bsz:25-freidok-1542403
Rechteinformation
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Letzte Aktualisierung
25.03.2025, 13:50 MEZ

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  • 2024

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