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Classification and Uncertainty Quantification of Corrupted Data using
  Semi-Supervised Autoencoders

Classification and Uncertainty Quantification of Corrupted Data using Semi-Supervised Autoencoders

27 May 2021
Philipp Joppich
S. Dorn
Oliver De Candido
Wolfgang Utschick
Jakob Knollmuller
ArXivPDFHTML

Papers citing "Classification and Uncertainty Quantification of Corrupted Data using Semi-Supervised Autoencoders"

2 / 2 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
285
9,138
0
06 Jun 2015
NIFTY - Numerical Information Field Theory - a versatile Python library
  for signal inference
NIFTY - Numerical Information Field Theory - a versatile Python library for signal inference
M. Selig
M. Bell
H. Junklewitz
N. Oppermann
M. Reinecke
M. Greiner
C. Pachajoa
T. Ensslin
63
63
0
18 Jan 2013
1