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Kanil Patel
Kanil Patel
Bosch Center for Artificial Intelligence
Bestätigte E-Mail-Adresse bei de.bosch.com
Titel
Zitiert von
Zitiert von
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Deep Learning-based Object Classification on Automotive Radar Spectra
K Patel, K Rambach, T Visentin, D Rusev, M Pfeiffer, B Yang
IEEE Radar Conference 2019, 2019
472019
Multi-Class Uncertainty Calibration via Mutual Information Maximization-based Binning
K Patel, W Beluch, B Yang, M Pfeiffer, D Zhang
International Conference on Learning Representations, ICLR 2021, 2020
142020
On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration
K Patel, W Beluch, D Zhang, M Pfeiffer, B Yang
International Conference on Pattern Recognition, ICPR 2020, 2019
82019
Better fiber ODFs from suboptimal data with autoencoder based regularization
K Patel, S Groeschel, T Schultz
International Conference on Medical Image Computing and Computer-Assisted …, 2018
32018
Investigation of Uncertainty of Deep Learning-based Object Classification on Radar Spectra
K Patel, W Beluch, K Rambach, AE Cozma, M Pfeiffer, B Yang
2021 IEEE Radar Conference (RadarConf21), 1-6, 2021
12021
Improving Uncertainty of Deep Learning-based Object Classification on Radar Spectra using Label Smoothing
K Patel, W Beluch, K Rambach, M Pfeiffer, B Yang
2022 IEEE Radar Conference (RadarConf22), 1-6, 2022
2022
Locating and/or classifying objects based on radar data, with improved reliability at different distances
K Patel, K Rambach, M Pfeiffer
US Patent 11,269,059, 2022
2022
Classification model calibration
D Zhang, K Patel, WH Beluch
US Patent App. 17/240,108, 2021
2021
Deep Learning-based Object Classification on Automotive Radar Spectra
TV Visentin, DR Rusev, BY Yang, MP Pfeiffer, KR Rambach, KP Patel
2019
Better Fiber ODFs From Suboptimal Data With Autoencoder Based Regularization Download PDF
K Patel, S Gröschel, T Schultz
Multi-Class Uncertainty Calibration via Mutual Information Maximization-based Binning Download PDF
K Patel, W Beluch, B Yang, M Pfeiffer, D Zhang
International Conference on Learning Representations, ICLR 2021, 0
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