Neural architecture search with bayesian optimization and network transformation = 베이지안 최적화 및 네트워크 변형을 이용한 신경망 구조 탐색

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For an artificial neural network to perform well, it is necessary to design an appropriate internal hierarchy, which requires a lot of time and expertise. Therefore, a method to automate the neural network structure design has been proposed, and a structure that can perform better than the human structure has been found. However, this method has a problem that takes a vast computational resource, because of huge search space and repetitive training. In this thesis, we propose a method to search the neural network efficiently by constructing the search range systematically through network transformation and Bayesian optimization method. Our method can finds a better convolution neural network architecture than the other methods under limited number of network evaluation conditions.
Advisors
Chong, Songresearcher정송researcher
Description
한국과학기술원 :전기및전자공학부,
Publisher
한국과학기술원
Issue Date
2019
Identifier
325007
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2019.2,[iii, 18 p. :]

Keywords

Convolutional neural network▼aneural architecture search▼abayesian optimization▼anetwork transformation▼adeep learning; 컨볼루젼 신경망▼a신경망 구조 탐색▼a베이지안 최적화▼a네트워크 변형▼a딥 러닝

URI
http://hdl.handle.net/10203/266921
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=843412&flag=dissertation
Appears in Collection
EE-Theses_Master(석사논문)
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