2 citations · 6 across the 4 of their papers we have counts for
4 papers
Approximate, Adapt, Anonymize (3A): a Framework for Privacy Preserving Training Data Release for Machine Learning
Tamas Madl, Weijie Xu, Olivia Choudhury +1
The availability of large amounts of informative data is crucial for successful machine learning. However, in domains with sensitive information, the release of high-utility data w…
Smartphone-based paroxysmal atrial fibrillation monitoring with robust generalization
Tamas Madl, David Madl
Atrial fibrillation is increasingly prevalent, especially in the elderly, and challenging to detect due paroxysmal nature. Here, we propose novel computational methods based on hea…
Safe Semi-Supervised Learning of Sum-Product Networks
Martin Trapp, Tamas Madl, Robert Peharz +2
In several domains obtaining class annotations is expensive while at the same time unlabelled data are abundant. While most semi-supervised approaches enforce restrictive assumptio…
Deep neural heart rate variability analysis
Tamas Madl
Despite of the pain and limited accuracy of blood tests for early recognition of cardiovascular disease, they dominate risk screening and triage. On the other hand, heart rate vari…