{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00212208","sets":["1164:4179:10535:10649"]},"path":["10649"],"owner":"44499","recid":"212208","title":["事例ベース推論を行うニューラルモデルの説明性とハブ現象の関係"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-07-20"},"_buckets":{"deposit":"a1779e81-a2a1-4b09-9809-855bf476a7c7"},"_deposit":{"id":"212208","pid":{"type":"depid","value":"212208","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"事例ベース推論を行うニューラルモデルの説明性とハブ現象の関係","author_link":["540906","540902","540903","540905","540904"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"事例ベース推論を行うニューラルモデルの説明性とハブ現象の関係"}]},"item_type_id":"4","publish_date":"2021-07-20","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東北大学"},{"subitem_text_value":"奈良先端科学技術大学院大学／理化学研究所"},{"subitem_text_value":"理化学研究所／東北大学"},{"subitem_text_value":"理化学研究所／東北大学"},{"subitem_text_value":"東北大学／理化学研究所"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Tohoku University","subitem_text_language":"en"},{"subitem_text_value":"Nara Institute of Science and Technology / RIKEN","subitem_text_language":"en"},{"subitem_text_value":"RIKEN / Tohoku University","subitem_text_language":"en"},{"subitem_text_value":"RIKEN / Tohoku University","subitem_text_language":"en"},{"subitem_text_value":"Tohoku 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 file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/212208/files/IPSJ-NL21249008.pdf","label":"IPSJ-NL21249008.pdf"},"date":[{"dateType":"Available","dateValue":"2023-07-20"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-NL21249008.pdf","filesize":[{"value":"1.4 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"23"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"a3b5fc9f-4bab-4e40-8f13-e4921fe6ac46","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"佐藤, 俊"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"大内, 啓樹"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"塙, 一晃"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"佐々木, 翔大"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"乾, 健太郎"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10115061","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":"2188-8779","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"ニューラルネットワークを用いたモデル（ニューラルモデル）によって，画像処理や自然言語処理の諸タスクにおける予測性能は飛躍的に向上した．一方で，「なぜモデルがそのような予測をしたのか」を理解することは，人間にとって極めて困難であることが指摘されている．予測の「説明性」に関する問題点に対して，k 近傍法のように訓練事例との類似度にもとづいて予測を行うモデルが近年注目を集めている．この種のモデルは事例ベースモデルと呼ばれ，予測への貢献度の高い訓練事例を予測根拠として提示することが容易であるという利点を持つ．しかし，k 近傍法においては，同じ訓練事例が複数のテスト事例の近傍事例として過度に重複して出現する「ハブ」と呼ばれる現象が度々観測される．これまでの研究で，ハブ現象が事例ベースニューラルモデルの説明性に与える影響は明らかになっていない．本研究では，画像と言語データを用いた分類問題において，ニューラルモデルの枠組みで k 近傍法を使用する場面を想定し，ハブ現象が予測の説明性に悪影響を与えることを定量的に示し，かつその問題の緩和策について明らかにする．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"10","bibliographic_titles":[{"bibliographic_title":"研究報告自然言語処理（NL）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2021-07-20","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"8","bibliographicVolumeNumber":"2021-NL-249"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":212208,"updated":"2025-01-19T17:34:29.629945+00:00","links":{},"created":"2025-01-19T01:13:12.164612+00:00"}