{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00234837","sets":["1164:5352:11553:11625"]},"path":["11625"],"owner":"44499","recid":"234837","title":["分布ロバストセーフ事例スクリーニングの提案および無限幅深層ニューラルネットワークへの応用"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-06-13"},"_buckets":{"deposit":"6bf29b21-9bf8-4830-ba65-47cf58536949"},"_deposit":{"id":"234837","pid":{"type":"depid","value":"234837","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"分布ロバストセーフ事例スクリーニングの提案および無限幅深層ニューラルネットワークへの応用","author_link":["640625","640644","640640","640628","640642","640641","640634","640639","640629","640646","640645","640626","640636","640630","640632","640635","640643","640638","640637","640631","640633","640627"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"分布ロバストセーフ事例スクリーニングの提案および無限幅深層ニューラルネットワークへの応用"},{"subitem_title":"Distributionally Robust Safe Sample Screening and Its Application to Infinite-width Deep Neural Networks","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"情報論的学習理論と機械学習2","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2024-06-13","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"名古屋大学"},{"subitem_text_value":"理化学研究所"},{"subitem_text_value":"名古屋大学"},{"subitem_text_value":"名古屋大学"},{"subitem_text_value":"名古屋大学"},{"subitem_text_value":"名古屋工業大学"},{"subitem_text_value":"理化学研究所"},{"subitem_text_value":"株式会社デンソー"},{"subitem_text_value":"株式会社デンソー"},{"subitem_text_value":"株式会社デンソー"},{"subitem_text_value":"名古屋大学 / 理化学研究所"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Nagoya University","subitem_text_language":"en"},{"subitem_text_value":"RIKEN","subitem_text_language":"en"},{"subitem_text_value":"Nagoya University","subitem_text_language":"en"},{"subitem_text_value":"Nagoya University","subitem_text_language":"en"},{"subitem_text_value":"Nagoya University","subitem_text_language":"en"},{"subitem_text_value":"Nagoya Institute of Technology","subitem_text_language":"en"},{"subitem_text_value":"RIKEN","subitem_text_language":"en"},{"subitem_text_value":"DENSO CORPORATION","subitem_text_language":"en"},{"subitem_text_value":"DENSO CORPORATION","subitem_text_language":"en"},{"subitem_text_value":"DENSO CORPORATION","subitem_text_language":"en"},{"subitem_text_value":"Nagoya University / RIKEN","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 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博幸"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"赤羽, 智志"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"大藏, 芳斗"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"田中, 智成"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"稲津, 佑"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"橋本, 典明"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"村山, 太郎"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"李, 翰柱"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"小嶋, 信矢"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"竹内, 一郎"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Tatsuya, Aoyama","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hiroyuki, Hanada","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Satoshi, Akahane","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yoshito, Okura","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Tomonari, Tanaka","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Yu, Inatsu","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Noriaki, Hashimoto","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Taro, Murayama","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Lee, Hanju","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Shinya, Kojima","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Ichiro, Takeuchi","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA12055912","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_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8590","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"近年の機械学習分野では，大規模データセットを扱う際の計算資源の不足が問題となっている．このような問題に対して，最適化計算前に不要な事例を取り除くことが出来るセーフ事例スクリーニングと呼ばれる手法が有効である．しかしながら，入力データ分布が変動する共変量シフト下では，従来のセーフ事例スクリーニング手法は不要な事例を適切に同定できない．そこで本研究では，主にサポートベクトルマシン（SVM）を対象に分布の変動に対してロバストに事例の削除を行うセーフ事例スクリーニングを提案する．計算機実験では無限幅深層ニューラルネットワークを用いて提案手法の妥当性を示す．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In machine learning, handling large datasets has been problematic in computational resources. For this issue, safe sample screening (SSS) is an effective solution to remove unnecessary samples before the optimization computation. However, conventional SSS cannot be applied under covariate shift, where the input data distribution changes. In this study, we propose a safe sample screening method that robustly removes unnecessary samples against distribution changes, mainly targeting support vector machines (SVM). Finally we experimentally demonstrate its validity for an infinite-width deep neural network.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告バイオ情報学（BIO）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-06-13","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"10","bibliographicVolumeNumber":"2024-BIO-78"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":234837,"updated":"2025-01-19T09:41:48.335290+00:00","links":{},"created":"2025-01-19T01:36:41.343874+00:00"}