20 citations · 36 across the 3 of their papers we have counts for
5 papers
Irregularly-Sampled Time Series Modeling with Spline Networks
Marin Biloš, Emanuel Ramneantu, Stephan Günnemann
Observations made in continuous time are often irregular and contain the missing values across different channels. One approach to handle the missing data is imputing it using spli…
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…
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…
Uncertainty on Asynchronous Time Event Prediction
Marin Biloš, Bertrand Charpentier, Stephan Günnemann
Asynchronous event sequences are the basis of many applications throughout different industries. In this work, we tackle the task of predicting the next event (given a history), an…
Intensity-Free Learning of Temporal Point Processes
Oleksandr Shchur, Marin Biloš, Stephan Günnemann
Temporal point processes are the dominant paradigm for modeling sequences of events happening at irregular intervals. The standard way of learning in such models is by estimating t…