Former Research Engineer
After completing his studies in applied computer science, Adrian Schwaiger started at Fraunhofer IKS in April 2019. He worked on improving the safety and interpretability of deep neural networks to enable their use for perception modules of autonomous systems.
by Adrian Schwaiger
Machine learning, and especially deep learning, can be used to enable many highly complex applications, such as autonomous driving. However, there are new challenges that need to be overcome to secure such systems. An overview.
In autonomous systems or medical engineering, uncertainty in machine learning (ML) processes needs to be reliably determined. That is why we are comparing four state-of-the-art methods of quantifying the uncertainty of deep neural networks (DNNs) in the context of safety-critical systems — so that the AI can learn when there is scope for doubt.
