36 citations · 90 across the 6 of their papers we have counts for
6 papers
GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings
Matthias Fey, Jan E. Lenssen, Frank Weichert +1
We present GNNAutoScale (GAS), a framework for scaling arbitrary message-passing GNNs to large graphs. GAS prunes entire sub-trees of the computation graph by utilizing historical…
Agent-based Simulation of Pedestrian Dynamics for Exposure Time Estimation in Epidemic Risk Assessment
Thomas Harweg, Daniel Bachmann, Frank Weichert
With the Corona Virus Disease 2019 (COVID-19) pandemic spreading across the world, protective measures for containing the virus are essential, especially as long as no vaccine or e…
Hierarchical Inter-Message Passing for Learning on Molecular Graphs
Matthias Fey, Jan-Gin Yuen, Frank Weichert
We present a hierarchical neural message passing architecture for learning on molecular graphs. Our model takes in two complementary graph representations: the raw molecular graph…
Adversarial Generation of Continuous Implicit Shape Representations
Marian Kleineberg, Matthias Fey, Frank Weichert
This work presents a generative adversarial architecture for generating three-dimensional shapes based on signed distance representations. While the deep generation of shapes has b…
Recognizing Cuneiform Signs Using Graph Based Methods
Nils M. Kriege, Matthias Fey, Denis Fisseler +2
The cuneiform script constitutes one of the earliest systems of writing and is realized by wedge-shaped marks on clay tablets. A tremendous number of cuneiform tablets have already…
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels
Matthias Fey, Jan Eric Lenssen, Frank Weichert +1
We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e.g., graphs or meshes. Our main…