{"title":"Memory Capacity of Neural Networks with Threshold and Rectified Linear Unit Activations","authors":"R. Vershynin","doi":"10.1137/20m1314884","DOIUrl":null,"url":null,"abstract":"Overwhelming theoretical and empirical evidence shows that mildly overparametrized neural networks---those with more connections than the size of the training data---are often able to memorize the ...","PeriodicalId":74797,"journal":{"name":"SIAM journal on mathematics of data science","volume":"19 1","pages":"1004-1033"},"PeriodicalIF":2.6000,"publicationDate":"2020-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"41","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SIAM journal on mathematics of data science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1137/20m1314884","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
引用次数: 41
Abstract
Overwhelming theoretical and empirical evidence shows that mildly overparametrized neural networks---those with more connections than the size of the training data---are often able to memorize the ...
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