{"title":"利用拓扑数据分析对深度神经网络解释的全面回顾","authors":"","doi":"10.1016/j.neucom.2024.128513","DOIUrl":null,"url":null,"abstract":"<div><p>Deep neural networks have achieved significant success across various fields, but their intrinsic black-box nature hinders the further development. Addressing the interpretability challenges, topological data analysis has emerged as a promising tool to reveal these complex models. In this work, we present a review of the emerging field of interpreting deep neural networks using topological data analysis. We organize the existing body of work into distinct analytical categories, highlighting interpretations based on the topology of data, network structural characteristics, network functional characteristics, and techniques derived from Mapper. The objective of this paper is to extract the research pattern of this area, and point out the future research direction.</p></div>","PeriodicalId":19268,"journal":{"name":"Neurocomputing","volume":null,"pages":null},"PeriodicalIF":5.5000,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A comprehensive review of deep neural network interpretation using topological data analysis\",\"authors\":\"\",\"doi\":\"10.1016/j.neucom.2024.128513\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Deep neural networks have achieved significant success across various fields, but their intrinsic black-box nature hinders the further development. Addressing the interpretability challenges, topological data analysis has emerged as a promising tool to reveal these complex models. In this work, we present a review of the emerging field of interpreting deep neural networks using topological data analysis. We organize the existing body of work into distinct analytical categories, highlighting interpretations based on the topology of data, network structural characteristics, network functional characteristics, and techniques derived from Mapper. The objective of this paper is to extract the research pattern of this area, and point out the future research direction.</p></div>\",\"PeriodicalId\":19268,\"journal\":{\"name\":\"Neurocomputing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.5000,\"publicationDate\":\"2024-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neurocomputing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0925231224012840\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neurocomputing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0925231224012840","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
A comprehensive review of deep neural network interpretation using topological data analysis
Deep neural networks have achieved significant success across various fields, but their intrinsic black-box nature hinders the further development. Addressing the interpretability challenges, topological data analysis has emerged as a promising tool to reveal these complex models. In this work, we present a review of the emerging field of interpreting deep neural networks using topological data analysis. We organize the existing body of work into distinct analytical categories, highlighting interpretations based on the topology of data, network structural characteristics, network functional characteristics, and techniques derived from Mapper. The objective of this paper is to extract the research pattern of this area, and point out the future research direction.
期刊介绍:
Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.