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Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve
  mixtures

Modeling continuous-time stochastic processes using N\mathcal{N}N-Curve mixtures

12 August 2019
Ronny Hug
Wolfgang Hubner
Michael Arens
ArXivPDFHTML

Papers citing "Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures"

1 / 1 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
245
9,042
0
06 Jun 2015
1