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        <identifier>oai:ipsj.ixsq.nii.ac.jp:00232707</identifier>
        <datestamp>2025-01-19T10:20:49Z</datestamp>
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          <dc:title>Tweedie’s formulaによるデノイジング・オートエンコーダの一般化</dc:title>
          <dc:title xml:lang="en">The Generalized Denoising Autoencoder with Tweedie’s Formula</dc:title>
          <jpcoar:creator>
            <jpcoar:creatorName>相島, 祐太</jpcoar:creatorName>
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          <jpcoar:creator>
            <jpcoar:creatorName>園田, 翔</jpcoar:creatorName>
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          <jpcoar:creator>
            <jpcoar:creatorName>磯部, 伸</jpcoar:creatorName>
          </jpcoar:creator>
          <jpcoar:creator>
            <jpcoar:creatorName>池田, 和司</jpcoar:creatorName>
          </jpcoar:creator>
          <jpcoar:creator>
            <jpcoar:creatorName xml:lang="en">Yuta, Aishima</jpcoar:creatorName>
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          <jpcoar:creator>
            <jpcoar:creatorName xml:lang="en">Sho, Sonoda</jpcoar:creatorName>
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          <jpcoar:creator>
            <jpcoar:creatorName xml:lang="en">Noboru, Isobe</jpcoar:creatorName>
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          <jpcoar:creator>
            <jpcoar:creatorName xml:lang="en">Kazushi, Ikeda</jpcoar:creatorName>
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          <datacite:description descriptionType="Other">デノイジング・オートエンコーダはデータ生成分布のスコア ∇ log ???? (????) を学習している．しかし，従来のデノイジング・オートエンコーダの理論研究はノイズ付加過程がガウス分布の場合のみが考察されていた．本研究では，ノイズ付加過程を指数型分布族に拡張することを考える．Tweedie’s formula を用いてデノイジング・オートエンコーダの最適解が周辺分布 ???? (????˜) =    ???? (????˜</datacite:description>
          <datacite:description descriptionType="Other">Denoising autoencoders learn the score of the data-generating distribution, i.e., ∇ log ???? (????). However, theoretical studies have discussed only the case when the corruption process is Gaussian. To generalize the previous results, in this study, we extend the class of distributions to an exponential family. By using Tweedie’s formula, we show that the generalized DAE learns score of marginal distribution ???? (????˜) = ∫ ???? (????˜</datacite:description>
          <dc:publisher xml:lang="ja">情報処理学会</dc:publisher>
          <datacite:date dateType="Issued">2024-02-25</datacite:date>
          <dc:language>jpn</dc:language>
          <dc:type rdf:resource="http://purl.org/coar/resource_type/c_18gh">technical report</dc:type>
          <jpcoar:identifier identifierType="URI">https://ipsj.ixsq.nii.ac.jp/records/232707</jpcoar:identifier>
          <jpcoar:sourceIdentifier identifierType="ISSN">2188-8701</jpcoar:sourceIdentifier>
          <jpcoar:sourceIdentifier identifierType="NCID">AA11131797</jpcoar:sourceIdentifier>
          <jpcoar:sourceTitle>研究報告コンピュータビジョンとイメージメディア（CVIM）</jpcoar:sourceTitle>
          <jpcoar:volume>2024-CVIM-237</jpcoar:volume>
          <jpcoar:issue>16</jpcoar:issue>
          <jpcoar:pageStart>1</jpcoar:pageStart>
          <jpcoar:pageEnd>5</jpcoar:pageEnd>
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