106 citations · 280 across the 16 of their papers we have counts for
30 papers
Unveiling the Sampling Density in Non-Uniform Geometric Graphs
Raffaele Paolino, Aleksandar Bojchevski, Stephan Günnemann +2
A powerful framework for studying graphs is to consider them as geometric graphs: nodes are randomly sampled from an underlying metric space, and any pair of nodes is connected if…
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…
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks
Marten Lienen, Stephan Günnemann
We propose a new method for spatio-temporal forecasting on arbitrarily distributed points. Assuming that the observed system follows an unknown partial differential equation, we de…
Is it all a cluster game? -- Exploring Out-of-Distribution Detection based on Clustering in the Embedding Space
Poulami Sinhamahapatra, Rajat Koner, Karsten Roscher +1
It is essential for safety-critical applications of deep neural networks to determine when new inputs are significantly different from the training distribution. In this paper, we…
Differentiable DAG Sampling
Bertrand Charpentier, Simon Kibler, Stephan Günnemann
We propose a new differentiable probabilistic model over DAGs (DP-DAG). DP-DAG allows fast and differentiable DAG sampling suited to continuous optimization. To this end, DP-DAG sa…
A Study of Joint Graph Inference and Forecasting
Daniel Zügner, François-Xavier Aubet, Victor Garcia Satorras +3
We study a recent class of models which uses graph neural networks (GNNs) to improve forecasting in multivariate time series. The core assumption behind these models is that there…