Report Open Access

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

Michael Weiss; Paolo Tonella


MARC21 XML Export

<?xml version='1.0' encoding='UTF-8'?>
<record xmlns="http://www.loc.gov/MARC21/slim">
  <leader>00000nam##2200000uu#4500</leader>
  <controlfield tag="005">20210702014817.0</controlfield>
  <controlfield tag="001">5055751</controlfield>
  <datafield tag="700" ind1=" " ind2=" ">
    <subfield code="u">Università della Svizzera italiana</subfield>
    <subfield code="a">Paolo Tonella</subfield>
  </datafield>
  <datafield tag="856" ind1="4" ind2=" ">
    <subfield code="s">658145</subfield>
    <subfield code="z">md5:4f6500a64a0b1d256f212f8fb2a44e68</subfield>
    <subfield code="u">https://zenodo.org/record/5055751/files/TR-Precrime-2021-04.pdf</subfield>
  </datafield>
  <datafield tag="542" ind1=" " ind2=" ">
    <subfield code="l">open</subfield>
  </datafield>
  <datafield tag="260" ind1=" " ind2=" ">
    <subfield code="c">2021-06-01</subfield>
  </datafield>
  <datafield tag="909" ind1="C" ind2="O">
    <subfield code="p">openaire</subfield>
    <subfield code="o">oai:zenodo.org:5055751</subfield>
  </datafield>
  <datafield tag="100" ind1=" " ind2=" ">
    <subfield code="u">Università della Svizzera italiana</subfield>
    <subfield code="a">Michael Weiss</subfield>
  </datafield>
  <datafield tag="245" ind1=" " ind2=" ">
    <subfield code="a">Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification</subfield>
  </datafield>
  <datafield tag="536" ind1=" " ind2=" ">
    <subfield code="c">787703</subfield>
    <subfield code="a">Self-assessment Oracles for Anticipatory Testing</subfield>
  </datafield>
  <datafield tag="540" ind1=" " ind2=" ">
    <subfield code="u">https://creativecommons.org/licenses/by/4.0/legalcode</subfield>
    <subfield code="a">Creative Commons Attribution 4.0 International</subfield>
  </datafield>
  <datafield tag="650" ind1="1" ind2="7">
    <subfield code="a">cc-by</subfield>
    <subfield code="2">opendefinition.org</subfield>
  </datafield>
  <datafield tag="520" ind1=" " ind2=" ">
    <subfield code="a">&lt;p&gt;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.&amp;nbsp;We present Uncertainty-Wizard, a tool that allows to quantify such uncertainty and confidence in&amp;nbsp; artificial neural networks.&amp;nbsp;It is built on top of the industry-leading tf.keras deep learning API and it provides a near-transparent and&amp;nbsp; easy to understand interface.&amp;nbsp;At the same time, it includes major performance optimizations that we benchmarked on two different machines and different configurations.&lt;/p&gt;</subfield>
  </datafield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="n">doi</subfield>
    <subfield code="i">isObsoletedBy</subfield>
    <subfield code="a">10.1109/ICST49551.2021.00056</subfield>
  </datafield>
  <datafield tag="773" ind1=" " ind2=" ">
    <subfield code="n">doi</subfield>
    <subfield code="i">isVersionOf</subfield>
    <subfield code="a">10.5281/zenodo.5055750</subfield>
  </datafield>
  <datafield tag="024" ind1=" " ind2=" ">
    <subfield code="a">10.5281/zenodo.5055751</subfield>
    <subfield code="2">doi</subfield>
  </datafield>
  <datafield tag="980" ind1=" " ind2=" ">
    <subfield code="a">publication</subfield>
    <subfield code="b">report</subfield>
  </datafield>
</record>
47
32
views
downloads
All versions This version
Views 4747
Downloads 3232
Data volume 21.1 MB21.1 MB
Unique views 4141
Unique downloads 3030

Share

Cite as