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    <title language="eng">Gaussian-Based and Outside-the-Box Runtime Monitoring Join Forces</title>
    <parentTitle language="eng">Runtime Verification: 24th International Conference, Proceedings</parentTitle>
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      <first_name>Vahid</first_name>
      <last_name>Hashemi</last_name>
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      <first_name>Erika</first_name>
      <last_name>Ábrahám</last_name>
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      <first_name>Jan</first_name>
      <last_name>Křetínský</last_name>
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      <first_name>Houssam</first_name>
      <last_name>Abbas</last_name>
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      <first_name>Sabine</first_name>
      <last_name>Rieder</last_name>
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      <first_name>Torsten</first_name>
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      <first_name>Jan</first_name>
      <last_name>Vorhoff</last_name>
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    <id>5375</id>
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    <pageNumber>16</pageNumber>
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    <title language="eng">Gaussian-Based and Outside-the-Box Runtime Monitoring Join Forces</title>
    <abstract language="eng">Since neural networks can make wrong predictions even with high confidence, monitoring their behavior at runtime is important, especially in safety-critical domains like autonomous driving. In this paper, we combine ideas from previous monitoring approaches based on observing the activation values of hidden neurons. In particular, we combine the Gaussian-based approach, which observes whether the current value of each monitored neuron is similar to typical values observed during training, and the Outside-the-Box monitor, which creates clusters of the acceptable activation values, and, thus, considers the correlations of the neurons' values. Our experiments evaluate the achieved improvement.</abstract>
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      <last_name>Hashemi</last_name>
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      <last_name>Křetínský</last_name>
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      <first_name>Sabine</first_name>
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    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
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