2 papers
cs.LG2022
Unaligned but Safe -- Formally Compensating Performance Limitations for Imprecise 2D Object Detection
Tobias Schuster, Emmanouil Seferis, Simon Burton +1
In this paper, we consider the imperfection within machine learning-based 2D object detection and its impact on safety. We address a special sub-type of performance limitations: th…
cs.LO2021
Logically Sound Arguments for the Effectiveness of ML Safety Measures
Chih-Hong Cheng, Tobias Schuster, Simon Burton
We investigate the issues of achieving sufficient rigor in the arguments for the safety of machine learning functions. By considering the known weaknesses of DNN-based 2D bounding…