{"id":235898,"updated":"2025-01-19T09:28:28.891577+00:00","links":{},"created":"2025-01-19T01:37:39.020616+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00235898","sets":["6504:11678:11689"]},"path":["11689"],"owner":"44499","recid":"235898","title":["Adaptive HMC: Improve Generation Quality of Score-based Generative Model"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-03-01"},"_buckets":{"deposit":"9ad6f132-35a8-4cce-a7c7-7da52da79d69"},"_deposit":{"id":"235898","pid":{"type":"depid","value":"235898","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Adaptive HMC: Improve Generation Quality of Score-based Generative Model","author_link":["644663","644662"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Adaptive HMC: Improve Generation Quality of Score-based Generative Model"}]},"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":"2024-03-01","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":"明大"},{"subitem_text_value":"明大"}]},"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/235898/files/IPSJ-Z86-7C-02.pdf","label":"IPSJ-Z86-7C-02.pdf"},"date":[{"dateType":"Available","dateValue":"2024-07-03"}],"format":"application/pdf","filename":"IPSJ-Z86-7C-02.pdf","filesize":[{"value":"464.5 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"005f554b-8b8d-4cf0-801c-1e7aabcefc56","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2024 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"韓, 宇"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"中村, 和幸"}],"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":"Score-based Generative Modelss (SGMs) is increasingly gaining attention for its ease of training. Langevin MCMC is commonly used in SGMs, but it introduces inefficient random walks. Hamiltonian Monte Carlo (HMC) is a deterministic MCMC method that utilizes an auxiliary variable scheme. Suppressing the random walk is a significant feature of HMC. Therefore, in this study, we employ HMC during the generation process of SGMs. We will present the experimental results of the proposed approach.","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"104","bibliographic_titles":[{"bibliographic_title":"第86回全国大会講演論文集"}],"bibliographicPageStart":"103","bibliographicIssueDates":{"bibliographicIssueDate":"2024-03-01","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2024"}]},"relation_version_is_last":true,"weko_creator_id":"44499"}}