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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.…
Document Type: | Article (unreviewed) |
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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): | Urheberrechtlich geschützt |
ArXiv Id: | http://arxiv.org/abs/2002.11429 |