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Data-driven discovery of interpretable causal relations for deep
  learning material laws with uncertainty propagation

Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty propagation

20 May 2021
Xiao Sun
B. Bahmani
Nikolaos N. Vlassis
WaiChing Sun
Yanxun Xu
    CML
    AI4CE
ArXivPDFHTML

Papers citing "Data-driven discovery of interpretable causal relations for deep learning material laws with uncertainty propagation"

3 / 3 papers shown
Title
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
243
9,042
0
06 Jun 2015
Causal Inference and Causal Explanation with Background Knowledge
Causal Inference and Causal Explanation with Background Knowledge
Christopher Meek
CML
196
628
0
20 Feb 2013
Improving neural networks by preventing co-adaptation of feature
  detectors
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton
Nitish Srivastava
A. Krizhevsky
Ilya Sutskever
Ruslan Salakhutdinov
VLM
237
7,597
0
03 Jul 2012
1