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Graph-based Reinforcement Learning for Active Learning in Real Time: An
  Application in Modeling River Networks

Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks

27 October 2020
X. Jia
Beiyu Lin
Jacob Aaron Zwart
J. Sadler
A. Appling
S. Oliver
J. Read
    OffRL
    AI4CE
ArXivPDFHTML

Papers citing "Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks"

3 / 3 papers shown
Title
Physics-aware Machine Learning Revolutionizes Scientific Paradigm for
  Machine Learning and Process-based Hydrology
Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology
Qingsong Xu
Yilei Shi
Jonathan Bamber
Ye Tuo
Ralf Ludwig
Xiao Xiang Zhu
AI4CE
18
9
0
08 Oct 2023
Active Learning for Graph Neural Networks via Node Feature Propagation
Active Learning for Graph Neural Networks via Node Feature Propagation
Yuexin Wu
Yichong Xu
Aarti Singh
Yiming Yang
A. Dubrawski
GNN
AI4CE
46
63
0
16 Oct 2019
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