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Deep, spatially coherent Inverse Sensor Models with Uncertainty
  Incorporation using the evidential Framework

Deep, spatially coherent Inverse Sensor Models with Uncertainty Incorporation using the evidential Framework

29 March 2019
Daniel Bauer
L. Kuhnert
L. Eckstein
    EDL
ArXivPDFHTML

Papers citing "Deep, spatially coherent Inverse Sensor Models with Uncertainty Incorporation using the evidential Framework"

3 / 3 papers shown
Title
Deep Radar Inverse Sensor Models for Dynamic Occupancy Grid Maps
Deep Radar Inverse Sensor Models for Dynamic Occupancy Grid Maps
Zihang Wei
Rujiao Yan
M. Schreier
21
1
0
21 May 2023
Interpretable Self-Aware Neural Networks for Robust Trajectory
  Prediction
Interpretable Self-Aware Neural Networks for Robust Trajectory Prediction
Masha Itkina
Mykel J. Kochenderfer
EDL
UQCV
26
26
0
16 Nov 2022
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
287
9,156
0
06 Jun 2015
1