6 citations · 9 across the 20 of their papers we have counts for
3 papers · 1 filter
Simplicial Representation Learning with Neural -Forms
Kelly Maggs, Celia Hacker, Bastian Rieck
Geometric deep learning extends deep learning to incorporate information about the geometry and topology data, especially in complex domains like graphs. Despite the popularity of…
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…
Topological Inductive Bias fosters Multiple Instance Learning in Data-Scarce Scenarios
Salome Kazeminia, Carsten Marr, Bastian Rieck
Multiple instance learning (MIL) is a framework for weakly supervised classification, where labels are assigned to sets of instances, i.e., bags, rather than to individual data poi…