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

文献类型：期刊论文

作者Chen, Jianhui1,4,5; Wang, Zuoren2,3,4; Liu, Cheng-Lin1,5
刊名CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY
出版日期2023-08-29
页码18
关键词artificial neural networks biologically plausible Laplacian-based graph smoothing lateral interaction machine learning
ISSN号2468-6557
DOI10.1049/cit2.12265
通讯作者Wang, Zuoren(zuorenwang@ion.ac.cn) ; Liu, Cheng-Lin(liucl@nlpr.ia.ac.cn)
英文摘要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.
WOS关键词VISUAL-CORTEX ; CLASSIFICATION ; MODEL
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
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001060157300001
出版者WILEY
资助机构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
源URL[http://ir.ia.ac.cn/handle/173211/53176]  
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, State Key Lab Multimodal Artificial Intelligence S, Inst Automat, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Sch Future Technol, Beijing, Peoples R China
3.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai, Peoples R China
4.Chinese Acad Sci, Inst Neurosci, Ctr Excellence Brain Sci & Intelligence Technol, State Key Lab Neurosci, Shanghai, Peoples R China
5.Univ Chinese Acad Sci, Beijing, 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.

入库方式： OAI收割

来源：自动化研究所

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