DNA computing inspired deep networks design

doi:10.1016/j.neucom.2019.11.098

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 模式分析与学习

	DNA computing inspired deep networks design
	Zhong, Guoqiang 1; Li, Tao 1; Jiao, Wencong 1; Wang, Li-Na 1; Dong, Junyu 1; Liu, Cheng-Lin2,3,4
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
	2020-03-21
卷号	382 页码:140-147
通讯作者	Zhong, Guoqiang(gqzhong@ouc.edu.cn)
摘要	Deep neural networks have gained state-of-the-art results in many applications, such as pattern recognition and computer vision. However, most of the deep neural networks are designed manually by researchers. This architecture design process is generally time consuming and needs much expertise. Hence, automatic neural network design becomes an important issue. In this paper, we propose a novel method, called DNA computing inspired networks design (DNAND), to automatically learn high performance deep networks. In DNAND, we use DNA strands to represent blocks of a model, and these DNA strands are reacted to construct the overall networks according to the base pairing principle. We also present the killing strategy, with which we stop training "bad" models if they fail to reach the specific accuracy threshold on the validation set, so as to reduce the computational cost and accelerate the learning process. Extensive experiments on image classification and detection data sets demonstrate the effectiveness of the proposed method. (C) 2019 Elsevier B.V. All rights reserved.
关键词	Deep neural networks DNA computing Automatic architecture design Image classification
DOI	10.1016/j.neucom.2019.11.098
关键词[WOS]	NEURAL-NETWORK ; EVOLUTIONARY ; OPTIMIZATION ; COMPUTATION
收录类别	SCI
语种	英语
资助项目	Major Project for New Generation of AI[2018AAA0100400] ; National Key R&D Program of China[2016YFC1401004] ; National Natural Science Foundation of China (NSFC)[41706010] ; Science and Technology Program of Qingdao[17-3-3-20-nsh] ; CERNET Innovation Project[NGII20170416] ; Ministry of Education of China[6141A020337] ; Fundamental Research Funds for the Central Universities of China ; Joint Fund of the Equipments Pre-Research[6141A020337] ; Major Project for New Generation of AI[2018AAA0100400] ; National Key R&D Program of China[2016YFC1401004] ; National Natural Science Foundation of China (NSFC)[41706010] ; Science and Technology Program of Qingdao[17-3-3-20-nsh] ; CERNET Innovation Project[NGII20170416] ; Ministry of Education of China[6141A020337] ; Fundamental Research Funds for the Central Universities of China ; Joint Fund of the Equipments Pre-Research[6141A020337]
项目资助者	Major Project for New Generation of AI ; National Key R&D Program of China ; National Natural Science Foundation of China (NSFC) ; Science and Technology Program of Qingdao ; CERNET Innovation Project ; Ministry of Education of China ; Fundamental Research Funds for the Central Universities of China ; Joint Fund of the Equipments Pre-Research
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000512881200015
出版者	ELSEVIER
引用统计	被引频次：12[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/28611
专题	多模态人工智能系统全国重点实验室_模式分析与学习
通讯作者	Zhong, Guoqiang
作者单位	1.Ocean Univ China, Dept Comp Sci & Technol, Qingdao 266100, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zhong, Guoqiang,Li, Tao,Jiao, Wencong,et al. DNA computing inspired deep networks design[J]. NEUROCOMPUTING,2020,382:140-147.
APA	Zhong, Guoqiang,Li, Tao,Jiao, Wencong,Wang, Li-Na,Dong, Junyu,&Liu, Cheng-Lin.(2020).DNA computing inspired deep networks design.NEUROCOMPUTING,382,140-147.
MLA	Zhong, Guoqiang,et al."DNA computing inspired deep networks design".NEUROCOMPUTING 382(2020):140-147.