{"created":"2025-01-19T00:34:44.026480+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00162711","sets":["6504:8672:8736"]},"path":["8736"],"owner":"6748","recid":"162711","title":["構造化ドロップアウトによる追加学習"],"pubdate":{"attribute_name":"公開日","attribute_value":"2016-03-10"},"_buckets":{"deposit":"dc72881e-4ab7-4a4f-977c-13b518853ced"},"_deposit":{"id":"162711","pid":{"type":"depid","value":"162711","revision_id":0},"owners":[6748],"status":"published","created_by":6748},"item_title":"構造化ドロップアウトによる追加学習","author_link":["317740","317741","317739"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"構造化ドロップアウトによる追加学習"}]},"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":"2016-03-10","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":"電機大"},{"subitem_text_value":"電機大"},{"subitem_text_value":"電機大"}]},"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/162711/files/IPSJ-Z78-4P-04.pdf","label":"IPSJ-Z78-4P-04.pdf"},"date":[{"dateType":"Available","dateValue":"2016-05-19"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-Z78-4P-04.pdf","filesize":[{"value":"359.2 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"8"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"276485c6-8f12-4ab6-b882-8123417cea1c","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2016 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"小澤, 優太"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"甲野, 佑"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"高橋, 達二"}],"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":"　深層学習は画像認識など一部の分野において人間のパフォーマンスを僅かに超え、また実用レベルの適用範囲はこれまで以上に広がっているが、学習に膨大な計算を必要とする。これはニューラルネットワークで元々困難であった追加学習をより難しいものにしている。　そこで本研究では、深層学習において過適合を防ぐテクニックであるDropoutに着目し、ランダムでなく構造を持った形式ニューロンの休止により、外部メモリやモジュールの切り替えといった外部機構を必要としない追加学習の方法を提案する。","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"372","bibliographic_titles":[{"bibliographic_title":"第78回全国大会講演論文集"}],"bibliographicPageStart":"371","bibliographicIssueDates":{"bibliographicIssueDate":"2016-03-10","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2016"}]},"relation_version_is_last":true,"weko_creator_id":"6748"},"links":{},"id":162711,"updated":"2025-01-20T11:40:55.647129+00:00"}