6 citations · 12 across the 24 of their papers we have counts for
4 papers · 1 filter
Reconciling AI Performance and Data Reconstruction Resilience for Medical Imaging
Alexander Ziller, Tamara T. Mueller, Simon Stieger +5
Artificial Intelligence (AI) models are vulnerable to information leakage of their training data, which can be highly sensitive, for example in medical imaging. Privacy Enhancing T…
How Low Can You Go? Surfacing Prototypical In-Distribution Samples for Unsupervised Anomaly Detection
Felix Meissen, Johannes Getzner, Alexander Ziller +4
Unsupervised anomaly detection (UAD) alleviates large labeling efforts by training exclusively on unlabeled in-distribution data and detecting outliers as anomalies. Generally, the…
SoK: Memorisation in machine learning
Dmitrii Usynin, Moritz Knolle, Georgios Kaissis
Quantifying the impact of individual data samples on machine learning models is an open research problem. This is particularly relevant when complex and high-dimensional relationsh…
Incentivising the federation: gradient-based metrics for data selection and valuation in private decentralised training
Dmitrii Usynin, Daniel Rueckert, Georgios Kaissis
Obtaining high-quality data for collaborative training of machine learning models can be a challenging task due to A) regulatory concerns and B) a lack of data owner incentives to…