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University of Cambridge > Talks.cam > Isaac Newton Institute Seminar Series > On ResNet type neural network architectures and their stability properties

## On ResNet type neural network architectures and their stability propertiesAdd to your list(s) Download to your calendar using vCal - Brynjulf Owren (Norwegian University of Science and Technology, Norwegian University of Science and Technology)
- Tuesday 02 November 2021, 09:00-10:00
- Seminar Room 2, Newton Institute.
If you have a question about this talk, please contact nobody. MDL - Mathematics of deep learning The study of neural networks as numerical approximations to continuous optimal control problems has gained some popularity after they were introduced by E (2017) and discussed in several other papers. This viewpoint paves the way for analysing the networks as dynamical systems and in particular to address their stability properties. This was the topic in a paper by Haber and Ruthotto (2017). - E, W. (2017). A proposal on machine learning via dynamical systems. Commun. Math. Stat. 5(1), 1–11
- Haber, E. & Ruthotto, L. (2017). Stable architectures for deep neural networks. Inverse Probl. 34(1)
- Benning, M., Celledoni, E., Ehrhardt, M. J., Owren, B. & Schönlieb, C.-B. (2019) Deep learning as optimal control problems: models and numerical methods. J. Comput. Dyn. 6(2), 171–198.
- E. Celledoni, M. J. Ehrhardt, C. Etmann, R. I. McLachlan, B. Owren, C.-B. Schonlieb and F. Sherry (2021). Structure preserving deep learning. European Journal of Applied Mathematics, 32(5), 888-936. doi:10.1017/S0956792521000139
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