Robustly and Optimaly Controlled Training Of neural Networks II (OCTON II)
Forskningsprojekt , 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).

Deltagare

Vincent Szolnoky (kontakt)

Doktorand vid Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Rebecka Jörnsten

Universitetslektor vid Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Balázs Adam Kulcsár

Biträdande professor vid Chalmers, Elektroteknik, System- och reglerteknik, Reglerteknik

Finansiering

Centiro

Finansierar Chalmers deltagande under 2020–2025

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Senast uppdaterat

2020-06-16