Robustly and Optimaly Controlled Training Of neural Networks (OCTON)
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)

vid Chalmers, Electrical Engineering, Systems and control, Automatic Control

Rebecka Jörnsten

Universitetslektor vid Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Balázs Adam Kulcsár

Biträdande professor vid Chalmers, Electrical Engineering, Systems and control, Automatic Control

Funding

Centiro

Funding Chalmers participation during 2019–2023

Related Areas of Advance and Infrastructure

Transport

Areas of Advance

More information

Latest update

2019-11-22