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GIANT Networks: Very Deep Fully Connected Neural Networks Applied to Microwave Problems

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Published in:IET Microwaves, Antennas & Propagation
Format: Online Article RSS Article
Published: 2026
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container_title IET Microwaves, Antennas & Propagation
description
discipline_display Technology & Engineering
discipline_facet Technology & Engineering
format Online Article
RSS Article
genre Journal Article
id rss_article:31194
institution FRELIP
journal_source_facet IET Microwaves, Antennas & Propagation
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle GIANT Networks: Very Deep Fully Connected Neural Networks Applied to Microwave Problems
Electronics
Technology & Engineering — Aerospace & Applied Tech
Technology & Engineering
sub_discipline_display Technology & Engineering — Aerospace & Applied Tech
sub_discipline_facet Technology & Engineering — Aerospace & Applied Tech
subject_display Electronics
Technology & Engineering — Aerospace & Applied Tech
Technology & Engineering
Electronics
Technology & Engineering — Aerospace & Applied Tech
Technology & Engineering
subject_facet Electronics
Technology & Engineering — Aerospace & Applied Tech
Technology & Engineering
title GIANT Networks: Very Deep Fully Connected Neural Networks Applied to Microwave Problems
title_auth GIANT Networks: Very Deep Fully Connected Neural Networks Applied to Microwave Problems
title_full GIANT Networks: Very Deep Fully Connected Neural Networks Applied to Microwave Problems
title_fullStr GIANT Networks: Very Deep Fully Connected Neural Networks Applied to Microwave Problems
title_full_unstemmed GIANT Networks: Very Deep Fully Connected Neural Networks Applied to Microwave Problems
title_short GIANT Networks: Very Deep Fully Connected Neural Networks Applied to Microwave Problems
title_sort giant networks: very deep fully connected neural networks applied to microwave problems
topic Electronics
Technology & Engineering — Aerospace & Applied Tech
Technology & Engineering
url https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/mia2.70077?af=R