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Agent-based Graph Neural Networks



Karolis Martinkus , Pál András Papp , Benedikt Schesch , Roger Wattenhofer

* 26 pages, 3 figures 

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A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications



Lukas Wolf , Ard Kastrati , Martyna Beata Płomecka , Jie-Ming Li , Dustin Klebe , Alexander Veicht , Roger Wattenhofer , Nicolas Langer

* 21 pages, Published at the Proceedings of the 39th International Conference on Machine Learning (ICML) 2022 

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Graph Neural Networks with Precomputed Node Features



Beni Egressy , Roger Wattenhofer


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DT+GNN: A Fully Explainable Graph Neural Network using Decision Trees



Peter Müller , Lukas Faber , Karolis Martinkus , Roger Wattenhofer


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Asynchronous Neural Networks for Learning in Graphs



Lukas Faber , Roger Wattenhofer


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SPECTRE : Spectral Conditioning Helps to Overcome the Expressivity Limits of One-shot Graph Generators



Karolis Martinkus , Andreas Loukas , Nathanaël Perraudin , Roger Wattenhofer

* 20 pages, 10 figures 

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A Theoretical Comparison of Graph Neural Network Extensions



Pál András Papp , Roger Wattenhofer


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The Price of Majority Support



Robin Fritsch , Roger Wattenhofer

* 9 pages, 2 figures 

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DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks



Pál András Papp , Karolis Martinkus , Lukas Faber , Roger Wattenhofer

* Published in the 35th Conference on Neural Information Processing Systems (NeurIPS 2021) 

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EEGEyeNet: a Simultaneous Electroencephalography and Eye-tracking Dataset and Benchmark for Eye Movement Prediction



Ard Kastrati , Martyna Beata Płomecka , Damián Pascual , Lukas Wolf , Victor Gillioz , Roger Wattenhofer , Nicolas Langer

* Published at NeurIPS 2021 Datasets and Benchmarks Track 

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