Papers may cover, but are not limited to the following topics:
Integrating domain knowledge into neural networks: Using domain knowledge is a key factor for robust and performant neural networks in cyber-physical systems. Examples how prior knowledge can be integrated into the neural network are the network architecture, additional data from simulations or by adding constraints to the loss function.
Certification of ML models: Performance, robustness and sensitivity of a model must be evaluated according to certain generally defined criteria. How can such criteria look like and how can ML models be certified by adopting them?
Automatic protection of ML models: For cyber-physical systems it is important that ML models do not deliver unexpected results. Nevertheless, how do solutions look like that put ML models into a fall-back mode? How can it be proven that the ML model is safe to use?
Automated Machine Learning: For the use of ML models in practice, it is essential that they can be trained, validated, and put into operation quickly to fulfill some performance criteria. AutoML is an efficient tool for that. How can AutoML be used to fulfill multicriterial objects, like interpretability or model size in combination with performance?
Information on participating / attending:
Submission Deadline: 6th of January 2023
Reviewer Feedback: 6th of February 2023
Camara-Ready Submission: 6th of March 2023
03/29/2023 - 03/31/2023
Am Sandtorkai 27
Scientists and scholars
Information technology, Mechanical engineering
Types of events:
Conference / symposium / (annual) conference
Event is free:
Language of the text:
URL of this event: http://idw-online.de/en/event73131
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