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Improving Uncertainty Quantification of Variance Networks by
  Tree-Structured Learning

Improving Uncertainty Quantification of Variance Networks by Tree-Structured Learning

24 December 2022
Wenxuan Ma
Xing Yan
Kun Zhang
    UQCV
ArXivPDFHTML

Papers citing "Improving Uncertainty Quantification of Variance Networks by Tree-Structured Learning"

3 / 3 papers shown
Title
DEUP: Direct Epistemic Uncertainty Prediction
DEUP: Direct Epistemic Uncertainty Prediction
Salem Lahlou
Moksh Jain
Hadi Nekoei
V. Butoi
Paul Bertin
Jarrid Rector-Brooks
Maksym Korablyov
Yoshua Bengio
PER
UQLM
UQCV
UD
195
80
0
16 Feb 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
268
5,635
0
05 Dec 2016
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
247
9,042
0
06 Jun 2015
1