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Bayesian Inference with Anchored Ensembles of Neural Networks, and
  Application to Exploration in Reinforcement Learning

Bayesian Inference with Anchored Ensembles of Neural Networks, and Application to Exploration in Reinforcement Learning

29 May 2018
Tim Pearce
Nicolas Anastassacos
Mohamed H. Zaki
A. Neely
    BDL
    UQCV
ArXivPDFHTML

Papers citing "Bayesian Inference with Anchored Ensembles of Neural Networks, and Application to Exploration in Reinforcement Learning"

8 / 8 papers shown
Title
Exploration in Deep Reinforcement Learning: A Survey
Exploration in Deep Reinforcement Learning: A Survey
Pawel Ladosz
Lilian Weng
Minwoo Kim
H. Oh
OffRL
28
324
0
02 May 2022
Trust Issues: Uncertainty Estimation Does Not Enable Reliable OOD
  Detection On Medical Tabular Data
Trust Issues: Uncertainty Estimation Does Not Enable Reliable OOD Detection On Medical Tabular Data
Dennis Ulmer
L. Meijerink
Giovanni Cina
OOD
13
64
0
06 Nov 2020
Scaling active inference
Scaling active inference
Alexander Tschantz
Manuel Baltieri
A. Seth
Christopher L. Buckley
BDL
AI4CE
19
68
0
24 Nov 2019
Fully Bayesian Recurrent Neural Networks for Safe Reinforcement Learning
Fully Bayesian Recurrent Neural Networks for Safe Reinforcement Learning
Matthew Benatan
Edward O. Pyzer-Knapp
BDL
24
6
0
08 Nov 2019
Estimating Risk and Uncertainty in Deep Reinforcement Learning
Estimating Risk and Uncertainty in Deep Reinforcement Learning
W. Clements
B. V. Delft
Benoît-Marie Robaglia
Reda Bahi Slaoui
Sébastien Toth
30
96
0
23 May 2019
Ensemble Model Patching: A Parameter-Efficient Variational Bayesian
  Neural Network
Ensemble Model Patching: A Parameter-Efficient Variational Bayesian Neural Network
Oscar Chang
Yuling Yao
David Williams-King
Hod Lipson
BDL
UQCV
32
8
0
23 May 2019
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
5,683
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
287
9,156
0
06 Jun 2015
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