{"title":"线性系统的非线性计算","authors":"Peter L. McMahon","doi":"10.1038/s41567-024-02531-y","DOIUrl":null,"url":null,"abstract":"Nonlinearity is crucial for sophisticated tasks in machine learning but is often difficult to engineer outside of electronics. By encoding the inputs in parameters of the system, linear systems can realize efficiently trainable nonlinear computations.","PeriodicalId":19100,"journal":{"name":"Nature Physics","volume":"20 9","pages":"1365-1366"},"PeriodicalIF":17.6000,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Nonlinear computation with linear systems\",\"authors\":\"Peter L. McMahon\",\"doi\":\"10.1038/s41567-024-02531-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Nonlinearity is crucial for sophisticated tasks in machine learning but is often difficult to engineer outside of electronics. By encoding the inputs in parameters of the system, linear systems can realize efficiently trainable nonlinear computations.\",\"PeriodicalId\":19100,\"journal\":{\"name\":\"Nature Physics\",\"volume\":\"20 9\",\"pages\":\"1365-1366\"},\"PeriodicalIF\":17.6000,\"publicationDate\":\"2024-07-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature Physics\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.nature.com/articles/s41567-024-02531-y\",\"RegionNum\":1,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PHYSICS, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature Physics","FirstCategoryId":"101","ListUrlMain":"https://www.nature.com/articles/s41567-024-02531-y","RegionNum":1,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
Nonlinearity is crucial for sophisticated tasks in machine learning but is often difficult to engineer outside of electronics. By encoding the inputs in parameters of the system, linear systems can realize efficiently trainable nonlinear computations.
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The journal presents two main research paper formats: Letters and Articles. Alongside primary research, Nature Physics serves as a central source for valuable information within the physics community through Review Articles, News & Views, Research Highlights covering crucial developments across the physics literature, Commentaries, Book Reviews, and Correspondence.
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