Converting tabular data into images for deep learning with convolutional neural networks
Converting tabular data into images for deep learning with convolutional neural networks
About this item
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Author / Creator
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States) , Los Alamos National Lab. (LANL), Los Alamos, NM (United States) , Argonne National Lab. (ANL), Argonne, IL (United States) , Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States) , Zhu, Yitan , Brettin, Thomas , Xia, Fangfang , Partin, Alexander , Shukla, Maulik , Yoo, Hyunseung , Evrard, Yvonne A. , Doroshow, James H. and Stevens, Rick L.
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
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Publication information
Publisher
London: Nature Publishing Group UK
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More information
Scope and Contents
Contents
Convolutional neural networks (CNNs) have been successfully used in many applications where important information about data is embedded in the order of features, such as speech and imaging. However, most tabular data do not assume a spatial relationship between features, and thus are unsuitable for modeling using CNNs. To meet this challenge, we d...
Alternative Titles
Full title
Converting tabular data into images for deep learning with convolutional neural networks
Authors, Artists and Contributors
Author / Creator
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Argonne National Lab. (ANL), Argonne, IL (United States)
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Zhu, Yitan
Brettin, Thomas
Xia, Fangfang
Partin, Alexander
Shukla, Maulik
Yoo, Hyunseung
Evrard, Yvonne A.
Doroshow, James H.
Stevens, Rick L.
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_37c2f916e6fa45c08315225e5a9e05de
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_37c2f916e6fa45c08315225e5a9e05de
Other Identifiers
ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-021-90923-y
