Robustly and Optimally Controlled Training Of neural Networks I (OCTON I)
Research Project, 2019
– 2023
This project aims at developing novel data training methods for network of function approximators (such as neural networks) based on robust and optimal control theory. The main idea is to utilize approximate neural tangent kernel parametrization in order to dynamically constrain training objectives. In this project we will develop novel methods that accounts for non-traditional training objectives (other than mean square prediction error) and corrupted data sequence. The latter claims for robustification. Conservativeness, stability of training, guaranteed rate of convergence, scalable numerical optimization routines will be developed.
Participants
Viktor Andersson (contact)
Chalmers, Electrical Engineering, Systems and control
Rebecka Jörnsten
Chalmers, Mathematical Sciences, Applied Mathematics and Statistics
Balázs Adam Kulcsár
Chalmers, Electrical Engineering, Systems and control
Funding
Centiro
Funding Chalmers participation during 2019–2023
Related Areas of Advance and Infrastructure
Transport
Areas of Advance