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Status: Bibliographieeintrag
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Verfasst von:Haußmann, Manuel [VerfasserIn]   i
 Hamprecht, Fred [VerfasserIn]   i
 Kandemir, Melih [VerfasserIn]   i
Titel:Deep active learning with adaptive acquisition
Verf.angabe:Manuel Haußmann, Fred Hamprecht and Melih Kandemir
E-Jahr:2019
Jahr:27 Jun 2019
Umfang:7 S.
Fussnoten:Gesehen am 13.07.2022
Titel Quelle:Enthalten in: De.arxiv.org
Ort Quelle:[S.l.] : Arxiv.org, 1991
Jahr Quelle:2019
Band/Heft Quelle:(2019), Artikel-ID 1906.11471, Seite 1-7
Abstract:Model selection is treated as a standard performance boosting step in many machine learning applications. Once all other properties of a learning problem are fixed, the model is selected by grid search on a held-out validation set. This is strictly inapplicable to active learning. Within the standardized workflow, the acquisition function is chosen among available heuristics a priori, and its success is observed only after the labeling budget is already exhausted. More importantly, none of the earlier studies report a unique consistently successful acquisition heuristic to the extent to stand out as the unique best choice. We present a method to break this vicious circle by defining the acquisition function as a learning predictor and training it by reinforcement feedback collected from each labeling round. As active learning is a scarce data regime, we bootstrap from a well-known heuristic that filters the bulk of data points on which all heuristics would agree, and learn a policy to warp the top portion of this ranking in the most beneficial way for the character of a specific data distribution. Our system consists of a Bayesian neural net, the predictor, a bootstrap acquisition function, a probabilistic state definition, and another Bayesian policy network that can effectively incorporate this input distribution. We observe on three benchmark data sets that our method always manages to either invent a new superior acquisition function or to adapt itself to the a priori unknown best performing heuristic for each specific data set.
DOI:doi:10.48550/arXiv.1906.11471
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext ; Verlag: https://doi.org/10.48550/arXiv.1906.11471
 Volltext: http://arxiv.org/abs/1906.11471
 DOI: https://doi.org/10.48550/arXiv.1906.11471
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Computer Science - Machine Learning
 Statistics - Machine Learning
K10plus-PPN:1810087864
Verknüpfungen:→ Sammelwerk

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