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        <datestamp>2025-01-19T10:20:21Z</datestamp>
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          <dc:title>時間点過程からの微分方程式と潜在イベント種の同時推定</dc:title>
          <dc:title xml:lang="en">Joint Estimation of Differential Equations and Latent Event Types from Temporal Point Processes</dc:title>
          <jpcoar:creator>
            <jpcoar:creatorName>宮澤, 脩一</jpcoar:creatorName>
          </jpcoar:creator>
          <jpcoar:creator>
            <jpcoar:creatorName>持橋, 大地</jpcoar:creatorName>
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          <jpcoar:creator>
            <jpcoar:creatorName xml:lang="en">Shuichi, Miyazawa</jpcoar:creatorName>
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          <jpcoar:creator>
            <jpcoar:creatorName xml:lang="en">Daichi, Mochihashi</jpcoar:creatorName>
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          <datacite:description descriptionType="Other">常微分方程式（ODE）は，様々な科学分野における現象の解釈を可能にする．ODE は多くの場合，数値データに適用されるが，われわれは連続時間上で発生する離散的なイベント系列（時間点過程）に対する ODE によるモデリング手法を提案した [1]．そこでは ODE によって記述される非線形力学系の構成要素の種類を示す，ラベル付きのイベント系列を要するが，現実にはこうしたラベルを明示的に持たない純粋なイベント系列も多い．一方で，特許出願における発明の抄録など，現実のイベントデータはしばしば付随する共変量を持ち，マークと呼ばれるこれらの情報は潜在的なイベント種の識別に有用である．そこで本研究では，ODE の構成要素を示すラベルを持たないイベント系列に対し，マークと ODE によって潜在的なイベント種を推定しつつ，データの生成過程をモデル化する方法を提案する．提案手法は，マークを用いてイベントに潜在ポアソン過程を割り当てる潜在ポアソン過程配分法 [2] を ODE で拡張したものと解釈できる．人工データにより提案手法が潜在イベント種および ODE パラメータを推定して復元できることを確認し，USPTO 特許出願データを用いて提案手法の実問題への適用可能性を示した．</datacite:description>
          <datacite:description descriptionType="Other">Ordinary differential equations (ODEs) help the interpretation of phenomena in various scientiﬁc ﬁelds. ODEs are often applied to numerical data, but we proposed a modeling method using ODEs for sequences of events occurring in continuous time (temporal point processes) [1]. Here, event series with labels indicating the type of components of the nonlinear dynamical system described by the ODEs are required, but in real settings, there are many event series that do not have such labels explicitly. Real event data is often accompanied by covariates, e.g., abstracts of inventions in patent applications. Such additional information, called marks, is useful for identifying latent event types. Therefore, we propose a method for modeling the generating process of event series by ODEs, using marks to estimate latent event types for event series without explicit labels indicating the components of the ODEs. The proposed method can be considered as an extension of latent Poisson process allocation [2], where each event is assigned to one of a set of latent Poisson processes, using ODEs. We demonstrated that the proposed method can estimate and recover latent event types and parameters of ODE using simulated data, and showed the applicability of the proposed method to a real problem using the USPTO patent dataset.</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/232733</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>42</jpcoar:issue>
          <jpcoar:pageStart>1</jpcoar:pageStart>
          <jpcoar:pageEnd>8</jpcoar:pageEnd>
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