3 citations · 3 across the 2 of their papers we have counts for
3 papers
cs.LG2021
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…
cs.LG2021★ 3 cited
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…
cs.LG2019
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.…