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PHS: A Toolbox for Parallel Hyperparameter Search

  • We introduce an open source python framework named PHS-Parallel Hyperparameter Search to enable hyperparameter optimization on numerous compute instances of any arbitrary python function. This is achieved with minimal modifications inside the target function. Possible applications appear in expensive to evaluate numerical computations which strongly depend on hyperparameters such as machineWe introduce an open source python framework named PHS-Parallel Hyperparameter Search to enable hyperparameter optimization on numerous compute instances of any arbitrary python function. This is achieved with minimal modifications inside the target function. Possible applications appear in expensive to evaluate numerical computations which strongly depend on hyperparameters such as machine learning. Bayesian optimization is chosen as a sample efficient method to propose the next query set of parameters.show moreshow less

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Metadaten
Document Type:Article (unreviewed)
Zitierlink: https://opus.hs-offenburg.de/4410
Bibliografische Angaben
Title (English):PHS: A Toolbox for Parallel Hyperparameter Search
Author:Peter Michael Habelitz, Janis KeuperStaff MemberORCiDGND
Year of Publication:2020
Page Number:5
Language:English
Inhaltliche Informationen
Institutes:Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Forschung / IMLA - Institute for Machine Learning and Analytics
Institutes:Bibliografie
Formale Angaben
Open Access: Open Access 
Licence (German):License LogoUrheberrechtlich geschützt
ArXiv Id:http://arxiv.org/abs/2002.11429