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Correlated Parameters to Accurately Measure Uncertainty in Deep Neural
  Networks

Correlated Parameters to Accurately Measure Uncertainty in Deep Neural Networks

2 April 2019
K. Posch
J. Pilz
    UQCV
    BDL
ArXivPDFHTML

Papers citing "Correlated Parameters to Accurately Measure Uncertainty in Deep Neural Networks"

5 / 5 papers shown
Title
Reducing Overconfidence Predictions for Autonomous Driving Perception
Reducing Overconfidence Predictions for Autonomous Driving Perception
Gledson Melotti
C. Premebida
Jordan J. Bird
Diego Resende Faria
Nuno Gonccalves
8
7
0
16 Feb 2022
Probabilistic Approach for Road-Users Detection
Probabilistic Approach for Road-Users Detection
Gledson Melotti
Weihao Lu
Pedro Conde
Dezong Zhao
A. Asvadi
Nuno Gonçalves
C. Premebida
27
2
0
02 Dec 2021
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
276
5,661
0
05 Dec 2016
Bayesian Convolutional Neural Networks with Bernoulli Approximate
  Variational Inference
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference
Y. Gal
Zoubin Ghahramani
UQCV
BDL
197
745
0
06 Jun 2015
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
285
9,138
0
06 Jun 2015
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