2 citations · 2 across the 4 of their papers we have counts for
4 papers
Opening Knowledge Gaps Drives Scientific Progress
Kara Kedrick, Wenlong Yang, Thomas Gebhart +2
Knowledge production is often viewed as an endogenous process in which discovery arises through the recombination of existing theories, findings, and concepts. Yet not all recombin…
Sheaf theory: from deep geometry to deep learning
Anton Ayzenberg, Thomas Gebhart, German Magai +1
This paper provides an overview of the applications of sheaf theory in deep learning, data science, and computer science in general. The primary text of this work serves as a frien…
ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain
Guillermo Bernárdez, Lev Telyatnikov, Marco Montagna +70
This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learnin…
Feature Propagation on Knowledge Graphs using Cellular Sheaves
John Cobb, Thomas Gebhart
Many inference tasks on knowledge graphs, including relation prediction, operate on knowledge graph embeddings -- vector representations of the vertices (entities) and edges (relat…