{"created":"2025-01-19T01:29:31.826058+00:00","updated":"2025-01-19T11:19:20.433591+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00230020","sets":["6504:11436:11440"]},"path":["11440"],"owner":"44499","recid":"230020","title":["再帰型ニューラルネットワーク中の重要なユニット抽出"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-02-16"},"_buckets":{"deposit":"2538ebf1-2895-44d7-9d5c-e817b5cebd80"},"_deposit":{"id":"230020","pid":{"type":"depid","value":"230020","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"再帰型ニューラルネットワーク中の重要なユニット抽出","author_link":["618841","618842"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"再帰型ニューラルネットワーク中の重要なユニット抽出"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"人工知能と認知科学","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2023-02-16","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"石川高専"},{"subitem_text_value":"石川高専"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/230020/files/IPSJ-Z85-6S-01.pdf","label":"IPSJ-Z85-6S-01.pdf"},"date":[{"dateType":"Available","dateValue":"2023-11-17"}],"format":"application/pdf","filename":"IPSJ-Z85-6S-01.pdf","filesize":[{"value":"482.8 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"9a5c638e-21cf-4224-a18d-39fae9d35ff4","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"森, 春介"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"金寺, 登"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"ニューラルネットワークは医療やセキュリティなど人間の生活に大きな影響を与えている．しかし，ニューラルネットワークのモデルは複雑であり，人間が解釈することは難しく，実質的なブラックボックスとなっている．そのため，ニューラルネットワークを安全に使用するために，その予測根拠の説明可能性は重要である．この問題の解決に向けて，全結合型や畳み込みニューラルネットワークの処理を可視化する方法が提案された．それはネットワーク出力をユニット出力で偏微分した値の変動によって，重要なユニットを特定するというものである．本研究では，同様の手法が再帰型ニューラルネットワーク（RNN）にも適用可能か調査した結果を報告する．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"450","bibliographic_titles":[{"bibliographic_title":"第85回全国大会講演論文集"}],"bibliographicPageStart":"449","bibliographicIssueDates":{"bibliographicIssueDate":"2023-02-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2023"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":230020,"links":{}}