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Exploiting Uncertainties from Ensemble Learners to Improve
  Decision-Making in Healthcare AI

Exploiting Uncertainties from Ensemble Learners to Improve Decision-Making in Healthcare AI

12 July 2020
Yingshui Tan
Baihong Jin
Xiangyu Yue
Yuxin Chen
Alberto L. Sangiovanni-Vincentelli
ArXivPDFHTML

Papers citing "Exploiting Uncertainties from Ensemble Learners to Improve Decision-Making in Healthcare AI"

5 / 5 papers shown
Title
Trust-informed Decision-Making Through An Uncertainty-Aware Stacked
  Neural Networks Framework: Case Study in COVID-19 Classification
Trust-informed Decision-Making Through An Uncertainty-Aware Stacked Neural Networks Framework: Case Study in COVID-19 Classification
Hassan Gharoun
M. S. Khorshidi
Fang Chen
Amir H. Gandomi
30
0
0
19 Sep 2024
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
5,695
0
05 Dec 2016
Neural Architecture Search with Reinforcement Learning
Neural Architecture Search with Reinforcement Learning
Barret Zoph
Quoc V. Le
274
5,331
0
05 Nov 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
287
9,167
0
06 Jun 2015
Near Optimal Bayesian Active Learning for Decision Making
Near Optimal Bayesian Active Learning for Decision Making
Shervin Javdani
Yuxin Chen
Amin Karbasi
Andreas Krause
J. Andrew Bagnell
S. Srinivasa
169
57
0
24 Feb 2014
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