52 citations · 152 across the 17 of their papers we have counts for
Showing 2023Show all
3 papers · 1 filter
cs.LG2023★ 2 cited
Curvature-based Pooling within Graph Neural Networks
Cedric Sanders, Andreas Roth, Thomas Liebig
Over-squashing and over-smoothing are two critical issues, that limit the capabilities of graph neural networks (GNNs). While over-smoothing eliminates the differences between node…
cs.LG2023
Rank Collapse Causes Over-Smoothing and Over-Correlation in Graph Neural Networks
Andreas Roth, Thomas Liebig
Our study reveals new theoretical insights into over-smoothing and feature over-correlation in graph neural networks. Specifically, we demonstrate that with increased depth, node r…
cs.LG2023
Distributed LSTM-Learning from Differentially Private Label Proportions
Timon Sachweh, Daniel Boiar, Thomas Liebig
Data privacy and decentralised data collection has become more and more popular in recent years. In order to solve issues with privacy, communication bandwidth and learning from sp…