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Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification

Michael Weiss; Paolo Tonella

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Michael Weiss</dc:creator>
  <dc:creator>Paolo Tonella</dc:creator>
  <dc:description>Uncertainty and confidence have been shown to be useful metrics in a wide variety of techniques proposed for deep learning testing, including test data selection and system supervision. We present Uncertainty-Wizard, a tool that allows to quantify such uncertainty and confidence in  artificial neural networks. It is built on top of the industry-leading tf.keras deep learning API and it provides a near-transparent and  easy to understand interface. At the same time, it includes major performance optimizations that we benchmarked on two different machines and different configurations.</dc:description>
  <dc:title>Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification</dc:title>
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