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Robustness and generalization performance of Deep Learning Models on Cyber-Physical Systems

Robustness and generalization performance of Deep Learning Models on Cyber-Physical Systems

Industrial AI Podcast vor 2 Jahren

We are back from the summer break. In this episode, Peter Seeberg talks to Prof. Dr. Oliver Niggemann about his study Robustness and Generalization Performance of Deep Learning Models on Cyber-Physical Systems.

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Industrial AI Podcast

350 Episoden

How the industry can benefit from AI technologies and what it takes to do so.

Wissenschaft Technologie Wirtschaft

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Deep learning (DL) models have seen increased attention for time
series forecasting, yet the application on cyber-physical systems
(CPS) is hindered by the lacking robustness of these methods. Thus,
this study evaluates the robustness and generalization performance
of DL architectures on multivariate time series data from CPS.
Peter Seeberg talked to the authors. Thanks for listening. We
welcome suggestions for topics, criticism and a few stars on Apple,
Spotify and Co. We thank our partner [Siemens
](https://new.siemens.com/global/en/products/automation/topic-areas/artificial-intelligence-in-industry.html)
Olivers Paper: https://arxiv.org/abs/2306.07737 Thanks to Women in
AI and Robotics ([more](https://www.womeninairobotics.de/)) PLEASE
fill out the survey from Gabriel Krummenacher and the ETH Zurich
[https://www.zuehlke.com/en/machine-learning](https://www.zuehlke.com/en/machine-learning)
We thank our team: Barbara, Anne and Simon!