22 citations · 67 across the 9 of their papers we have counts for
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2 papers · 1 filter
cs.LG2020★ 2 cited
Scalable Normalizing Flows for Permutation Invariant Densities
Marin Biloš, Stephan Günnemann
Modeling sets is an important problem in machine learning since this type of data can be found in many domains. A promising approach defines a family of permutation invariant densi…
cs.LG2020★ 15 cited
Deep Representation Learning and Clustering of Traffic Scenarios
Nick Harmening, Marin Biloš, Stephan Günnemann
Determining the traffic scenario space is a major challenge for the homologation and coverage assessment of automated driving functions. In contrast to current approaches that are…