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Qualitative Analysis of Monte Carlo Dropout

Qualitative Analysis of Monte Carlo Dropout

3 July 2020
Ronald Seoh
    UQCV
    BDL
ArXivPDFHTML

Papers citing "Qualitative Analysis of Monte Carlo Dropout"

4 / 4 papers shown
Title
Data-Driven Prediction and Uncertainty Quantification of PWR Crud-Induced Power Shift Using Convolutional Neural Networks
Data-Driven Prediction and Uncertainty Quantification of PWR Crud-Induced Power Shift Using Convolutional Neural Networks
Aidan Furlong
Farah Alsafadi
S. Palmtag
Andrew Godfrey
Xu Wu
62
1
0
27 Jun 2024
AI Security for Geoscience and Remote Sensing: Challenges and Future
  Trends
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends
Yonghao Xu
Tao Bai
Weikang Yu
Shizhen Chang
P. M. Atkinson
Pedram Ghamisi
AAML
45
47
0
19 Dec 2022
Bayesian Convolutional Neural Networks with Bernoulli Approximate
  Variational Inference
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference
Y. Gal
Zoubin Ghahramani
UQCV
BDL
213
745
0
06 Jun 2015
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
289
9,167
0
06 Jun 2015
1