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Paper Abstract and Keywords
Presentation 2022-06-27 14:25
A Bagging Method to Improve the Accuracy of Gaussian Process Regression for Neural Architecture Search
Rion Hada, Masao Okita, Fumihiko Ino (Osaka Univ.) NC2022-2 IBISML2022-2
Abstract (in Japanese) (See Japanese page) 
(in English) The goal of this study is to improve performance estimation for neural network architectures in neural architecture search (NAS), which leverages Bayesian optimization with Gaussian process regression.
To achieve this goal, we propose a bagging method to boost the accuracy of Gaussian process regression by controlling over-fitting.
Aiming to reduce the estimation error with Gaussian process regression, the proposed method extends the acquire function for Bayesian optimization in an existing NAS method: the extended acquire function iteratively estimates the inference accuracy of the target architecture with different supervised datasets and returns the median accuracy.
For the rest, as with the existing method, the proposed method searches neural architectures efficiently by limiting the architectures to be actually trained only to those estimated to show high inference accuracy by Bayesian optimization.
Experimental results show that the proposed method increased Spearman's rank correlation coefficient between an estimated ranking and the true ranking of inference accuracy for 100 neural architectures from 0.772 to 0.829.
This indicates that the proposed method is useful for precisely estimating the inference accuracy of neural architectures.
Keyword (in Japanese) (See Japanese page) 
(in English) NAS (neural architecture search) / surrogate model / Bayesian optimization / Gaussian process regression / overfitting / bagging (bootstrap aggregating) / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 90, IBISML2022-2, pp. 6-13, June 2022.
Paper # IBISML2022-2 
Date of Issue 2022-06-20 (NC, IBISML) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF NC2022-2 IBISML2022-2

Conference Information
Committee NC IBISML IPSJ-BIO IPSJ-MPS  
Conference Date 2022-06-27 - 2022-06-29 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To IBISML 
Conference Code 2022-06-NC-IBISML-BIO-MPS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Bagging Method to Improve the Accuracy of Gaussian Process Regression for Neural Architecture Search 
Sub Title (in English)  
Keyword(1) NAS (neural architecture search)  
Keyword(2) surrogate model  
Keyword(3) Bayesian optimization  
Keyword(4) Gaussian process regression  
Keyword(5) overfitting  
Keyword(6) bagging (bootstrap aggregating)  
Keyword(7)  
Keyword(8)  
1st Author's Name Rion Hada  
1st Author's Affiliation Osaka University (Osaka Univ.)
2nd Author's Name Masao Okita  
2nd Author's Affiliation Osaka University (Osaka Univ.)
3rd Author's Name Fumihiko Ino  
3rd Author's Affiliation Osaka University (Osaka Univ.)
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Speaker Author-1 
Date Time 2022-06-27 14:25:00 
Presentation Time 25 minutes 
Registration for IBISML 
Paper # NC2022-2, IBISML2022-2 
Volume (vol) vol.122 
Number (no) no.89(NC), no.90(IBISML) 
Page pp.6-13 
#Pages 8 
Date of Issue 2022-06-20 (NC, IBISML) 

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