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Deep-learning in the bioimaging wild: Handling ambiguous data with
  deepflash2

Deep-learning in the bioimaging wild: Handling ambiguous data with deepflash2

12 November 2021
M. Griebel
Dennis Segebarth
N. Stein
Nina Schukraft
P. Tovote
R. Blum
C. Flath
    MedIm
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Papers citing "Deep-learning in the bioimaging wild: Handling ambiguous data with deepflash2"

3 / 3 papers shown
Title
A disciplined approach to neural network hyper-parameters: Part 1 --
  learning rate, batch size, momentum, and weight decay
A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay
L. Smith
208
1,019
0
26 Mar 2018
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
5,661
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
285
9,138
0
06 Jun 2015
1