no code implementations • 21 Oct 2022 • David Reeb, Kanil Patel, Karim Barsim, Martin Schiegg, Sebastian Gerwinn
Assessing the validity of a real-world system with respect to given quality criteria is a common yet costly task in industrial applications due to the vast number of required real-world tests.
no code implementations • 27 Sep 2021 • Kanil Patel, William Beluch, Kilian Rambach, Michael Pfeiffer, Bin Yang
The focus of this article is to learn deep radar spectra classifiers which offer robust real-time uncertainty estimates using label smoothing during training.
no code implementations • 1 Jun 2021 • Kanil Patel, William Beluch, Kilian Rambach, Adriana-Eliza Cozma, Michael Pfeiffer, Bin Yang
Deep learning (DL) has recently attracted increasing interest to improve object type classification for automotive radar. In addition to high accuracy, it is crucial for decision making in autonomous vehicles to evaluate the reliability of the predictions; however, decisions of DL networks are non-transparent.
1 code implementation • ICLR 2021 • Kanil Patel, William Beluch, Bin Yang, Michael Pfeiffer, Dan Zhang
The goal of this paper is to resolve the identified issues of HB in order to provide calibrated confidence estimates using only a small holdout calibration dataset for bin optimization while preserving multi-class ranking accuracy.
no code implementations • 16 Dec 2019 • Kanil Patel, William Beluch, Dan Zhang, Michael Pfeiffer, Bin Yang
Uncertainty estimates help to identify ambiguous, novel, or anomalous inputs, but the reliable quantification of uncertainty has proven to be challenging for modern deep networks.