11 citations · 12 across the 2 of their papers we have counts for
3 papers · 1 filter
Adapting deep generative approaches for getting synthetic data with realistic marginal distributions
Kiana Farhadyar, Federico Bonofiglio, Daniela Zoeller +1
Synthetic data generation is of great interest in diverse applications, such as for privacy protection. Deep generative models, such as variational autoencoders (VAEs), are a popul…
Deep generative models in DataSHIELD
Stefan Lenz, Harald Binder
The best way to calculate statistics from medical data is to use the data of individual patients. In some settings, this data is difficult to obtain due to privacy restrictions. In…
Modeling Activity Tracker Data Using Deep Boltzmann Machines
Martin Treppner, Stefan Lenz, Harald Binder +1
Commercial activity trackers are set to become an essential tool in health research, due to increasing availability in the general population. The corresponding vast amounts of mos…