1k citations · 4.2k across the 120 of their papers we have counts for
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Reliable Graph Neural Networks via Robust Aggregation
Simon Geisler, Daniel Zügner, Stephan Günnemann
Perturbations targeting the graph structure have proven to be extremely effective in reducing the performance of Graph Neural Networks (GNNs), and traditional defenses such as adve…
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable?
Anna-Kathrin Kopetzki, Bertrand Charpentier, Daniel Zügner +2
Dirichlet-based uncertainty (DBU) models are a recent and promising class of uncertainty-aware models. DBU models predict the parameters of a Dirichlet distribution to provide fast…
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…
ThingML+ Augmenting Model-Driven Software Engineering for the Internet of Things with Machine Learning
Armin Moin, Stephan Rössler, Stephan Günnemann
In this paper, we present the current position of the research project ML-Quadrat, which aims to extend the methodology, modeling language and tool support of ThingML - an open sou…
From Things' Modeling Language (ThingML) to Things' Machine Learning (ThingML2)
Armin Moin, Stephan Rössler, Marouane Sayih +1
In this paper, we illustrate how to enhance an existing state-of-the-art modeling language and tool for the Internet of Things (IoT), called ThingML, to support machine learning on…
Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More
Aleksandar Bojchevski, Johannes Gasteiger, Stephan Günnemann
Existing techniques for certifying the robustness of models for discrete data either work only for a small class of models or are general at the expense of efficiency or tightness.…
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…
Scene Graph Reasoning for Visual Question Answering
Marcel Hildebrandt, Hang Li, Rajat Koner +2
Visual question answering is concerned with answering free-form questions about an image. Since it requires a deep linguistic understanding of the question and the ability to assoc…
Reachable Sets of Classifiers and Regression Models: (Non-)Robustness Analysis and Robust Training
Anna-Kathrin Kopetzki, Stephan Günnemann
Neural networks achieve outstanding accuracy in classification and regression tasks. However, understanding their behavior still remains an open challenge that requires questions t…
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts
Bertrand Charpentier, Daniel Zügner, Stephan Günnemann
Accurate estimation of aleatoric and epistemic uncertainty is crucial to build safe and reliable systems. Traditional approaches, such as dropout and ensemble methods, estimate unc…
Graph Hawkes Neural Network for Forecasting on Temporal Knowledge Graphs
Zhen Han, Yunpu Ma, Yuyi Wang +2
The Hawkes process has become a standard method for modeling self-exciting event sequences with different event types. A recent work has generalized the Hawkes process to a neurall…
Directional Message Passing for Molecular Graphs
Johannes Gasteiger, Janek Groß, Stephan Günnemann
Graph neural networks have recently achieved great successes in predicting quantum mechanical properties of molecules. These models represent a molecule as a graph using only the d…