GUM-compliant, analytical uncertainty propagation in deep neural networks
This repository contains my internship report which I created during the winter semester 2022 under the kind supervision of Sascha Eichstädt. The internship took place as part of my Master of Science degree in Mathematics on the topic:
GUM-compliant, analytical uncertainty propagation in deep neural networks
The linked PDF is automatically compiled after each push and thus always updated. It gets deleted one week after the last push but can always be regenerated through reexecution of the respective pipeline.
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Attribution
Idea and template for the GitLab-CI configuration including the Docker image used originate from Alexander Kammeyer.