1 citations · 2 across the 4 of their papers we have counts for
5 papers
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…
Detecting Spatial Dependence in Transcriptomics Data using Vectorised Persistence Diagrams
Katharina Limbeck, Bastian Rieck
Evaluating spatial patterns in data is an integral task across various domains, including geostatistics, astronomy, and spatial tissue biology. The analysis of transcriptomics data…
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…