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    <title>Pinboard (Vaguery)</title>
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    <description>recent bookmarks from Vaguery</description>
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  </channel><item rdf:about="https://arxiv.org/abs/1811.00547">
    <title>[1811.00547] Geometric Mean of Partial Positive Definite Matrices with Missing Entries</title>
    <dc:date>2019-02-19T11:44:18+00:00</dc:date>
    <link>https://arxiv.org/abs/1811.00547</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[In this paper the geometric mean of partial positive definite matrices with missing entries is considered. The weighted geometric mean of two sets of positive matrices is defined, and we show whether such a geometric mean holds certain properties which the weighted geometric mean of two positive definite matrices satisfies. Additionally, counterexamples demonstrate that certain properties do not hold. A Loewner order on partial Hermitian matrices is also defined. The known results for the maximum determinant positive completion are developed with an integral representation, and the results are applied to the weighted geometric mean of two partial positive definite matrices with missing entries. Moreover, a relationship between two positive definite completions is established with respect to their determinants, showing relationship between their entropy for a zero-mean,multivariate Gaussian distribution. Computational results as well as one application are shown.
]]></description>
<dc:subject>matrices to-understand inference proof looking-to-see to-write-about consider:algorithms missing-data data-cleaning</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:48ec3e3145bd/</dc:identifier>
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    <title>[1606.04130] Modeling Missing Data in Clinical Time Series with RNNs</title>
    <dc:date>2016-10-03T09:33:20+00:00</dc:date>
    <link>https://arxiv.org/abs/1606.04130</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA[We demonstrate a simple strategy to cope with missing data in sequential inputs, addressing the task of multilabel classification of diagnoses given clinical time series. Collected from the pediatric intensive care unit (PICU) at Children's Hospital Los Angeles, our data consists of multivariate time series of observations. The measurements are irregularly spaced, leading to missingness patterns in temporally discretized sequences. While these artifacts are typically handled by imputation, we achieve superior predictive performance by treating the artifacts as features. Unlike linear models, recurrent neural networks can realize this improvement using only simple binary indicators of missingness. For linear models, we show an alternative strategy to capture this signal. Training models on missingness patterns only, we show that for some diseases, what tests are run can as predictive as the results themselves.
]]></description>
<dc:subject>neural-networks time-series generative-models missing-data synthesis machine-learning RNN nudge-targets consider:representation consider:genetic-programming-equivalents</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:Vaguery/b:aea321d6bd8b/</dc:identifier>
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<item rdf:about="http://arxiv.org/abs/1005.2197">
    <title>[1005.2197] Scalable Tensor Factorizations for Incomplete Data</title>
    <dc:date>2010-05-16T11:54:08+00:00</dc:date>
    <link>http://arxiv.org/abs/1005.2197</link>
    <dc:creator>Vaguery</dc:creator><description><![CDATA["Our numerical studies suggest that the proposed CP-WOPT approach is accurate and scalable. CP-WOPT can recover the underlying factors successfully with large amounts of missing data, e.g., 90% missing entries for tensors of size 50 × 40 × 30. We have also studied how CP-WOPT can scale to problems of larger sizes, e.g., 1000 × 1000 × 1000, and recover CP factors from large, sparse tensors with 99.5% missing data.…"
]]></description>
<dc:subject>statistics numerical-methods missing-data scientific-computing algorithms</dc:subject>
<dc:identifier>https://pinboard.in/u:Vaguery/b:ab4a219c6d78/</dc:identifier>
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