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

Michael Weiss; Paolo Tonella


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{
  "description": "<p>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.&nbsp;We present Uncertainty-Wizard, a tool that allows to quantify such uncertainty and confidence in&nbsp; artificial neural networks.&nbsp;It is built on top of the industry-leading tf.keras deep learning API and it provides a near-transparent and&nbsp; easy to understand interface.&nbsp;At the same time, it includes major performance optimizations that we benchmarked on two different machines and different configurations.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Universit\u00e0 della Svizzera italiana", 
      "@type": "Person", 
      "name": "Michael Weiss"
    }, 
    {
      "affiliation": "Universit\u00e0 della Svizzera italiana", 
      "@type": "Person", 
      "name": "Paolo Tonella"
    }
  ], 
  "headline": "Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2021-06-01", 
  "url": "https://zenodo.org/record/5055751", 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.5055751", 
  "@id": "https://doi.org/10.5281/zenodo.5055751", 
  "@type": "ScholarlyArticle", 
  "name": "Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification"
}
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