{"created":"2025-01-18T23:46:58.469189+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00101357","sets":["1164:5064:7447:7571"]},"path":["7571"],"owner":"11","recid":"101357","title":["補助関数法によるGaussian-Bernoulli RBMの学習アルゴリズムの検討"],"pubdate":{"attribute_name":"公開日","attribute_value":"2014-05-17"},"_buckets":{"deposit":"cb67cbd7-2ec7-4989-b6f8-8452c04ca29d"},"_deposit":{"id":"101357","pid":{"type":"depid","value":"101357","revision_id":0},"owners":[11],"status":"published","created_by":11},"item_title":"補助関数法によるGaussian-Bernoulli RBMの学習アルゴリズムの検討","author_link":["0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"補助関数法によるGaussian-Bernoulli RBMの学習アルゴリズムの検討"}]},"item_type_id":"4","publish_date":"2014-05-17","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京大学大学院情報理工学系研究科"},{"subitem_text_value":"東京大学大学院情報理工学系研究科／日本電信電話株式会社NTTコミュニケーション科学基礎研究所"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"The University of Tokyo","subitem_text_language":"en"},{"subitem_text_value":"The University of Tokyo / NTT Communication Science Laboratories","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"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/101357/files/IPSJ-MUS14103040.pdf"},"date":[{"dateType":"Available","dateValue":"2016-05-17"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-MUS14103040.pdf","filesize":[{"value":"693.8 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"21"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"60d5ae52-f029-4e9a-aba6-cb6d9c287dcd","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2014 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"高宗典玄"},{"creatorName":"亀岡弘和"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10438388","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"近年，深層学習 (Deep learning) の有効性は音声認識をはじめ様々な分野で示されており，その重要な一要素として，制約付きボルツマンマシン (RBM) による pre-training がある．実数の観測データを取り扱うための Gaussian-Bernoulli RBM というモデルがあり，その学習アルゴリズムとして，最急降下法を基とした Contrastive Divergence 法が提案されてきた．そこで，本発表ではその学習問題に対して，経験的に高速で安定に収束する補助関数法による更新アルゴリズムを提案する．小規模な人工データによる実験を行い，その挙動に対して提案法と従来法を比較し議論する．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告音楽情報科学（MUS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2014-05-17","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"40","bibliographicVolumeNumber":"2014-MUS-103"}]},"relation_version_is_last":true,"weko_creator_id":"11"},"id":101357,"updated":"2025-01-21T11:15:52.887043+00:00","links":{}}