This talk explores key developments in neural network architectures. The discussion begins with an examination of Residual Neural Networks and their relation to Neural Ordinary Differential Equations (Neural ODEs). We briefly review some of the advantages, and limitations of Neural ODEs. We also consider possible variations of Residual Neural Networks and their higher-order Neural ODE representation. Furthermore, we explore the possibility of networks equipped with average nonlinear programming techniques, which might be alternatives to addressing the deep learning issues. Finally, we highlight some potential practical applications.

How to join online

You can join online via Zoom, using the following link:
https://uni-kl-de.zoom.us/j/63123116305?pwd=Yko3WU9ZblpGR3lGUkVTV1kzMCtUUT09

Referent: Amin Jafarimoghaddam, Department of Aerospace Engineering, Universidad Carlos III, Madrid, Spain

Zeit: 16:00 Uhr

Ort: Hybrid (Room 32-349 and via Zoom)