{"links":{},"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00108486","sets":["6504:6505:7806"]},"path":["7806"],"owner":"6748","recid":"108486","title":["再帰結合神経回路モデルへのスパース構造導入による学習能力の向上"],"pubdate":{"attribute_name":"公開日","attribute_value":"2011-03-02"},"_buckets":{"deposit":"9f2c42a6-6b09-48ca-b33c-6a055f1e492e"},"_deposit":{"id":"108486","pid":{"type":"depid","value":"108486","revision_id":0},"owners":[6748],"status":"published","created_by":6748},"item_title":"再帰結合神経回路モデルへのスパース構造導入による学習能力の向上","author_link":["20608","20612","20611","20610","20609"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"再帰結合神経回路モデルへのスパース構造導入による学習能力の向上"}]},"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":"2011-03-02","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":"京大"},{"subitem_text_value":"京大"},{"subitem_text_value":"理研"},{"subitem_text_value":"京大"},{"subitem_text_value":"京大"}]},"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/108486/files/IPSJ-Z73-1Q-4.pdf"},"date":[{"dateType":"Available","dateValue":"2014-12-17"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-Z73-1Q-4.pdf","filesize":[{"value":"179.7 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"5eabeec6-8661-44d5-aba6-f0a6dfbb3871","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2011 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"粟野皓光"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"尾形哲也"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"谷淳"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"高橋徹"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"奥乃博"}],"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":"本稿では，再帰結合型神経回路モデルへのスパース結合導入による性能向上を示す．近年，多様な時系列パターンを学習可能な，スパース結合型神経回路が着目を集めている．しかし通常これらのモデルは内部の結合重みが固定されており，学習能力には限界がある．我々は，異なる時定数のニューロン群からなる再帰結合型神経回路モデル，MTRNNの一部結合をスパース化し，全結合を学習可能としたモデルの性能評価を行った．スパース化率の異なるMTRNNに，アルファベット列からなる文章を学習させ，未知文及びノイズ文の認識・生成能力の評価を行った．実験の結果，スパース結合とすることで，全結合の場合よりも性能を向上できることが確認された．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"132","bibliographic_titles":[{"bibliographic_title":"第73回全国大会講演論文集"}],"bibliographicPageStart":"131","bibliographicIssueDates":{"bibliographicIssueDate":"2011-03-02","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2011"}]},"relation_version_is_last":true,"weko_creator_id":"6748"},"created":"2025-01-18T23:51:04.834213+00:00","updated":"2025-01-21T08:42:36.338691+00:00","id":108486}