Lydia Gauerhof

Orcid: 0000-0002-3504-0040

According to our database1, Lydia Gauerhof authored at least 14 papers between 2016 and 2022.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2022
Automating Safety Argument Change Impact Analysis for Machine Learning Components.
Proceedings of the 27th IEEE Pacific Rim International Symposium on Dependable Computing, 2022

2021
Testing Deep Learning-based Visual Perception for Automated Driving.
ACM Trans. Cyber Phys. Syst., 2021

On the Necessity of Explicit Artifact Links in Safety Assurance Cases for Machine Learning.
Proceedings of the IEEE International Symposium on Software Reliability Engineering, 2021

2020
Fault Injectors for TensorFlow: Evaluation of the Impact of Random Hardware Faults on Deep CNNs.
CoRR, 2020

Reverse Variational Autoencoder for Visual Attribute Manipulation and Anomaly Detection.
Proceedings of the IEEE Winter Conference on Applications of Computer Vision, 2020

Structuring the Safety Argumentation for Deep Neural Network Based Perception in Automotive Applications.
Proceedings of the Computer Safety, Reliability, and Security. SAFECOMP 2020 Workshops, 2020

Assuring the Safety of Machine Learning for Pedestrian Detection at Crossings.
Proceedings of the Computer Safety, Reliability, and Security, 2020

FACER: A Universal Framework for Detecting Anomalous Operation of Deep Neural Networks.
Proceedings of the 23rd IEEE International Conference on Intelligent Transportation Systems, 2020

Considering Reliability of Deep Learning Function to Boost Data Suitability and Anomaly Detection.
Proceedings of the 2020 IEEE International Symposium on Software Reliability Engineering Workshops, 2020

Bayesian Model for Trustworthiness Analysis of Deep Learning Classifiers.
Proceedings of the Workshop on Artificial Intelligence Safety 2020 co-located with the 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI 2020), 2020

2019
Confidence Arguments for Evidence of Performance in Machine Learning for Highly Automated Driving Functions.
Proceedings of the Computer Safety, Reliability, and Security, 2019

2018
Structuring Validation Targets of a Machine Learning Function Applied to Automated Driving.
Proceedings of the Computer Safety, Reliability, and Security, 2018

2017
Making the Case for Safety of Machine Learning in Highly Automated Driving.
Proceedings of the Computer Safety, Reliability, and Security, 2017

2016
Integration of a dynamic model in a driving simulator to meet requirements of various levels of automatization.
Proceedings of the 2016 IEEE Intelligent Vehicles Symposium, 2016


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