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2003.07775
Cited By

Deep generative models in DataSHIELD

Deep generative models in DataSHIELD

BMC Medical Research Methodology (BMC Med Res Methodol), 2020

11 March 2020

ArXiv (abs)PDF HTML

Papers citing "Deep generative models in DataSHIELD"

3 / 3 papers shown

SynthEval: A Framework for Detailed Utility and Privacy Evaluation of
Tabular Synthetic Data

SynthEval: A Framework for Detailed Utility and Privacy Evaluation of Tabular Synthetic Data

Peter Schneider-Kamp

303

38

0

24 Apr 2024

Can I trust my fake data -- A comprehensive quality assessment framework
for synthetic tabular data in healthcare

Can I trust my fake data -- A comprehensive quality assessment framework for synthetic tabular data in healthcare

Aleksandar Babic

Severin Elvatun

Narasimha Raghavan

Anne Kjersti Befring

225

51

0

24 Jan 2024

Combining propensity score methods with variational autoencoders for
generating synthetic data in presence of latent sub-groups

Combining propensity score methods with variational autoencoders for generating synthetic data in presence of latent sub-groups

Kiana Farhadyar

Maren Hackenberg

165

2

0

12 Dec 2023

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