상세정보
학술대회
A Multi-Scaled Method for Parallel Bayesian Optimization in Deep Predictive Analytics
- 발행일
- 202010
- 출처
- International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1306-1308
- 협약과제
- 20HS3300, 부하분산과 능동적 적시 대응을 위한 빅데이터 엣지 분석 기술 개발, 이용주
- 초록
- 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 제안 키워드
- Bayesian Optimization, Bayesian search, Experiment results, Multi-scaled, Parallel search, Scaling method, Search Algorithm(GSA), Search Space, Search direction, convergence speed, predictive analytics