12 citations · 23 across the 5 of their papers we have counts for
5 papers
Validation of Composite Systems by Discrepancy Propagation
David Reeb, Kanil Patel, Karim Barsim +2
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-…
Improving Uncertainty of Deep Learning-based Object Classification on Radar Spectra using Label Smoothing
Kanil Patel, William Beluch, Kilian Rambach +2
Object type classification for automotive radar has greatly improved with recent deep learning (DL) solutions, however these developments have mostly focused on the classification…
Investigation of Uncertainty of Deep Learning-based Object Classification on Radar Spectra
Kanil Patel, William Beluch, Kilian Rambach +3
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 ma…
Multi-Class Uncertainty Calibration via Mutual Information Maximization-based Binning
Kanil Patel, William Beluch, Bin Yang +2
Post-hoc multi-class calibration is a common approach for providing high-quality confidence estimates of deep neural network predictions. Recent work has shown that widely used sca…
On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration
Kanil Patel, William Beluch, Dan Zhang +2
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.…