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Directional Message Passing on Molecular Graphs via Synthetic Coordinates
Johannes Gasteiger, Chandan Yeshwanth, Stephan Günnemann
Graph neural networks that leverage coordinates via directional message passing have recently set the state of the art on multiple molecular property prediction tasks. However, the…
Intriguing Properties of Input-dependent Randomized Smoothing
Peter Súkeník, Aleksei Kuvshinov, Stephan Günnemann
Randomized smoothing is currently considered the state-of-the-art method to obtain certifiably robust classifiers. Despite its remarkable performance, the method is associated with…
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions
Nicholas Gao, Stephan Günnemann
Solving the Schrödinger equation is key to many quantum mechanical properties. However, an analytical solution is only tractable for single-electron systems. Recently, neural netwo…
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…
Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph)
Johannes C. Paetzold, Julian McGinnis, Suprosanna Shit +9
Biological neural networks define the brain function and intelligence of humans and other mammals, and form ultra-large, spatial, structured graphs. Their neuronal organization is…
Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question Answering
Rajat Koner, Hang Li, Marcel Hildebrandt +3
Visual Question Answering (VQA) is concerned with answering free-form questions about an image. Since it requires a deep semantic and linguistic understanding of the question and t…
On Out-of-distribution Detection with Energy-based Models
Sven Elflein, Bertrand Charpentier, Daniel Zügner +1
Several density estimation methods have shown to fail to detect out-of-distribution (OOD) samples by assigning higher likelihoods to anomalous data. Energy-based models (EBMs) are…
OODformer: Out-Of-Distribution Detection Transformer
Rajat Koner, Poulami Sinhamahapatra, Karsten Roscher +2
A serious problem in image classification is that a trained model might perform well for input data that originates from the same distribution as the data available for model train…
Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More
Johannes Gasteiger, Marten Lienen, Stephan Günnemann
The current best practice for computing optimal transport (OT) is via entropy regularization and Sinkhorn iterations. This algorithm runs in quadratic time as it requires the full…
ML-Quadrat & DriotData: A Model-Driven Engineering Tool and a Low-Code Platform for Smart IoT Services
Armin Moin, Andrei Mituca, Moharram Challenger +2
In this paper, we present ML-Quadrat, an open-source research prototype that is based on the Eclipse Modeling Framework (EMF) and the state of the art in the literature of Model-Dr…
Neural Temporal Point Processes: A Review
Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski +1
Temporal point processes (TPP) are probabilistic generative models for continuous-time event sequences. Neural TPPs combine the fundamental ideas from point process literature with…