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Conference Paper A Multi-Scaled Method for Parallel Bayesian Optimization in Deep Predictive Analytics
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Authors
Yong-Hyuk Moon, Yong-Ju Lee
Issue Date
2020-10
Citation
International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1306-1308
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICTC49870.2020.9289573
Abstract
This paper proposes a new roll-out scaling method for parallel Bayesian optimization and discusses how the proposed multi-scaled optimization guarantees a better convergence speed with outperformed accuracy than the conventional parallel search algorithms. Experiment results demonstrate that an entire search space can be efficiently reduced to more feasible subdomains. The performance of parallel Bayesian search can be further accelerated based on the interchangeable local evidence by properly adjusting three quantitative aspects in terms of space factorization, search direction, and architecture scaling.
KSP Keywords
Bayesian Optimization, Bayesian search, Experiment results, Multi-scaled, Parallel search, Scaling method, Search Algorithm(GSA), Search Space, Search direction, convergence speed, predictive analytics