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This code base is intended to serve as a starting point for interested researchers or practitioners to extend or apply the uncertainty propagation portion of the author's Master's thesis " GUM-compliant neural-network robustness verification".
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This project contains the implementation and examples of the publication MM/GW, CE, "A rejection Sampler extending GUM-S1", Metrologia, 2021 [TODO].
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The source code of the testbed implementation used in Gregor Hildermeier's work oin TESLA-protected one-way time synchronization
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MetHyInfra / Hydrogen-Real-Gas-Model
GNU General Public License v3.0 or laterUpdated -
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vaclab / json2couchdb
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Ingredients for conversion of a DCC xml structure to a printable pdf file
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This package accompanies my Master's thesis on GUM-compliant neural network robustness verification and provides an implementation using PySCIPOpt.
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Manuel Marschall / multivariate-ve
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ptb-843 / neural_networks_101
GNU General Public License v3.0 or laterUpdated -
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