Robustly and Optimaly Controlled Training Of neural Networks II (OCTON II)
Research Project , 2020 – 2025

This project aims at focusing on the development novel methods that accounts for non-traditional training objectives (other than mean square prediction error) and corrupted data sequence. This project is expected to result in faster and more accurate training solutions (classification, parameter estimation, short time prediction, tracking) than the currently available ones. The methods developed are application free and concentrates on the triplet of interpretability, robustness and network optimization via deeplearners (DNN).

Participants

Vincent Szolnoky (contact)

Doctoral Student at Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Rebecka Jörnsten

Senior Lecturer at Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Balázs Adam Kulcsár

Professor at Chalmers, Electrical Engineering, Systems and control, Automatic Control

Funding

Centiro

Funding Chalmers participation during 2020–2025

Related Areas of Advance and Infrastructure

Transport

Areas of Advance

More information

Latest update

2020-06-16