Lateral interaction by Laplacian-based graph smoothing for deep neural networks

doi:10.1049/cit2.12265

	Lateral interaction by Laplacian-based graph smoothing for deep neural networks
	Chen, Jianhui 1,2,3; Wang, Zuoren 1,4,5; Liu, Cheng-Lin2,3
发表期刊	CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY
ISSN	2468-6557
	2023-08-29
页码	18
通讯作者	Wang, Zuoren(zuorenwang@ion.ac.cn) ; Liu, Cheng-Lin(liucl@nlpr.ia.ac.cn)
摘要	Lateral interaction in the biological brain is a key mechanism that underlies higher cognitive functions. Linear self-organising map (SOM) introduces lateral interaction in a general form in which signals of any modality can be used. Some approaches directly incorporate SOM learning rules into neural networks, but incur complex operations and poor extendibility. The efficient way to implement lateral interaction in deep neural networks is not well established. The use of Laplacian Matrix-based Smoothing (LS) regularisation is proposed for implementing lateral interaction in a concise form. The authors' derivation and experiments show that lateral interaction implemented by SOM model is a special case of LS-regulated k-means, and they both show the topology-preserving capability. The authors also verify that LS-regularisation can be used in conjunction with the end-to-end training paradigm in deep auto-encoders. Additionally, the benefits of LS-regularisation in relaxing the requirement of parameter initialisation in various models and improving the classification performance of prototype classifiers are evaluated. Furthermore, the topologically ordered structure introduced by LS-regularisation in feature extractor can improve the generalisation performance on classification tasks. Overall, LS-regularisation is an effective and efficient way to implement lateral interaction and can be easily extended to different models.
关键词	artificial neural networks biologically plausible Laplacian-based graph smoothing lateral interaction machine learning
DOI	10.1049/cit2.12265
关键词[WOS]	VISUAL-CORTEX ; CLASSIFICATION ; MODEL
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China (NSFC)[61836014] ; STI2030-Major Projects[2022ZD0205100] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32010300] ; Shanghai Municipal Science and Technology Major Project[2018SHZDZX05] ; Innovation Academy of Artificial Intelligence, Chinese Academy of Sciences
项目资助者	National Natural Science Foundation of China (NSFC) ; STI2030-Major Projects ; Strategic Priority Research Program of Chinese Academy of Science ; Shanghai Municipal Science and Technology Major Project ; Innovation Academy of Artificial Intelligence, Chinese Academy of Sciences
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:001060157300001
出版者	WILEY
引用统计	被引频次：2[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/173211/53177
专题	多模态人工智能系统全国重点实验室
通讯作者	Wang, Zuoren; Liu, Cheng-Lin
作者单位	1.Chinese Acad Sci, Inst Neurosci, Ctr Excellence Brain Sci & Intelligence Technol, State Key Lab Neurosci, Shanghai, Peoples R China 2.Chinese Acad Sci, State Key Lab Multimodal Artificial Intelligence S, Inst Automat, Beijing, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Univ Chinese Acad Sci, Sch Future Technol, Beijing, Peoples R China 5.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai, Peoples R China
推荐引用方式 GB/T 7714	Chen, Jianhui,Wang, Zuoren,Liu, Cheng-Lin. Lateral interaction by Laplacian-based graph smoothing for deep neural networks[J]. CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY,2023:18.
APA	Chen, Jianhui,Wang, Zuoren,&Liu, Cheng-Lin.(2023).Lateral interaction by Laplacian-based graph smoothing for deep neural networks.CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY,18.
MLA	Chen, Jianhui,et al."Lateral interaction by Laplacian-based graph smoothing for deep neural networks".CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY (2023):18.