Lisa Jöckel | DeepAI

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∙ 05/24/2023

Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning

As the use of Artificial Intelligence (AI) components in cyber-physical ...

0 Janek Groß, et al. ∙

∙ 06/14/2022

Architectural patterns for handling runtime uncertainty of data-driven models in safety-critical perception

Data-driven models (DDM) based on machine learning and other AI techniqu...

0 Janek Groß, et al. ∙

∙ 02/10/2022

Integrating Testing and Operation-related Quantitative Evidences in Assurance Cases to Argue Safety of Data-Driven AI/ML Components

In the future, AI will increasingly find its way into systems that can p...

0 Michael Kläs, et al. ∙

∙ 01/10/2022

A Study on Mitigating Hard Boundaries of Decision-Tree-based Uncertainty Estimates for AI Models

Outcomes of data-driven AI models cannot be assumed to be always correct...

0 Pascal Gerber, et al. ∙

∙ 08/31/2021

Towards a Common Testing Terminology for Software Engineering and Data Science Experts

Analytical quality assurance, especially testing, is an integral part of...

0 Lisa Jöckel, et al. ∙

∙ 09/02/2019

Hardening of Artificial Neural Networks for Use in Safety-Critical Applications – A Mapping Study

Context: Across different domains, Artificial Neural Networks (ANNs) are...

0 Rasmus Adler, et al. ∙

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