Bayesian Contrastive Learning on Topological Structures
A.J. Möllers (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Elvin Isufi – Mentor (TU Delft - Multimedia Computing)
H.N. Kekkonen – Mentor (TU Delft - Statistics)
Vincent Fortuin – Mentor (Helmholtz AI)
Alexander Immer – Mentor (ETH Zürich)
Leo van van Iersel – Graduation committee member (TU Delft - Discrete Mathematics and Optimization)
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Abstract
In this thesis we develop a Bayesian approach to graph contrastive learning and propose a new uncertainty measure based on the disagreement in likelihood due to different positive samples. Moreover, we extend contrastive learning to simplicial complexes and show that it can be used to generate high-quality representations of edge flow data.