Search Results for author: Tamas Madl

Found 3 papers, 1 papers with code

Approximate, Adapt, Anonymize (3A): a Framework for Privacy Preserving Training Data Release for Machine Learning

no code implementations4 Jul 2023 Tamas Madl, Weijie Xu, Olivia Choudhury, Matthew Howard

Despite progress in differential privacy and generative modeling for privacy-preserving data release in the literature, only a few approaches optimize for machine learning utility: most approaches only take into account statistical metrics on the data itself and fail to explicitly preserve the loss metrics of machine learning models that are to be subsequently trained on the generated data.

Privacy Preserving Synthetic Data Generation

Safe Semi-Supervised Learning of Sum-Product Networks

1 code implementation10 Oct 2017 Martin Trapp, Tamas Madl, Robert Peharz, Franz Pernkopf, Robert Trappl

In several domains obtaining class annotations is expensive while at the same time unlabelled data are abundant.

Deep neural heart rate variability analysis

no code implementations29 Dec 2016 Tamas Madl

Despite of the pain and limited accuracy of blood tests for early recognition of cardiovascular disease, they dominate risk screening and triage.

Heart Rate Variability

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