1 citations · 2 across the 4 of their papers we have counts for
4 papers · 1 filter
Diversity Curves for Graph Representation Learning
Katharina Limbeck, Nadja Häusermann, Martin Carrasco +2
Graph-level representations are crucial tools for characterising structural differences between graphs. However, comparing graphs with different cardinalities, even when sampled fr…
Geometry-Aware Edge Pooling for Graph Neural Networks
Katharina Limbeck, Lydia Mezrag, Guy Wolf +1
Graph Neural Networks (GNNs) have shown significant success for graph-based tasks. Motivated by the prevalence of large datasets in real-world applications, pooling layers are cruc…
Metric Space Magnitude for Evaluating the Diversity of Latent Representations
Katharina Limbeck, Rayna Andreeva, Rik Sarkar +1
The magnitude of a metric space is a novel invariant that provides a measure of the 'effective size' of a space across multiple scales, while also capturing numerous geometrical pr…
Metric Space Magnitude and Generalisation in Neural Networks
Rayna Andreeva, Katharina Limbeck, Bastian Rieck +1
Deep learning models have seen significant successes in numerous applications, but their inner workings remain elusive. The purpose of this work is to quantify the learning process…