{"links":{},"id":196955,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00196955","sets":["6504:9795:9801"]},"path":["9801"],"owner":"6748","recid":"196955","title":["自然勾配法に基づく変分深層学習"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-02-28"},"_buckets":{"deposit":"0847fde9-deab-48e3-aa8d-7a8f5bbfd9d3"},"_deposit":{"id":"196955","pid":{"type":"depid","value":"196955","revision_id":0},"owners":[6748],"status":"published","created_by":6748},"item_title":"自然勾配法に基づく変分深層学習","author_link":["471981","471982","471983"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"自然勾配法に基づく変分深層学習"}]},"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":"2019-02-28","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":"東工大"},{"subitem_text_value":"東工大"},{"subitem_text_value":"東工大"}]},"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/196955/files/IPSJ-Z81-4U-05.pdf","label":"IPSJ-Z81-4U-05.pdf"},"date":[{"dateType":"Available","dateValue":"2019-05-28"}],"format":"application/pdf","filename":"IPSJ-Z81-4U-05.pdf","filesize":[{"value":"292.4 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"f1364fc9-02e8-44ca-b33c-73ddc240394f","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2019 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"中田, 光"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"大沢, 和樹"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"横田, 理央"}],"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":"深層学習は与えられた膨大なデータに対し柔軟な学習を可能にする一方、学習を汎化させ未知のデータに対しても精度を保つことが一つの大きな課題となる。近年では、ベイズ推定を深層学習に適用し、学習によって得られたニューラルネットワークの重みの不確かさを推定することにより学習を汎化させる試みが注目されつつある。Zhangらによって提案されたNoisy K-FACは、自然勾配法に基づく一種の変分推論を行うことによりベイズ推定を行う手法であり、学習が汎化することが示されている。本研究ではNoisy K-FACに着目し、重みの更新時に複数のサンプルを用いた場合の学習の変化ついて比較検証を行った。","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"462","bibliographic_titles":[{"bibliographic_title":"第81回全国大会講演論文集"}],"bibliographicPageStart":"461","bibliographicIssueDates":{"bibliographicIssueDate":"2019-02-28","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2019"}]},"relation_version_is_last":true,"weko_creator_id":"6748"},"created":"2025-01-19T01:01:26.907839+00:00","updated":"2025-01-19T22:34:36.029059+00:00"}