3 citations · 4 across the 16 of their papers we have counts for
3 papers · 1 filter
Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks
Pascal Mattia Esser, Leena Chennuru Vankadara, Debarghya Ghoshdastidar
In recent years, several results in the supervised learning setting suggested that classical statistical learning-theoretic measures, such as VC dimension, do not adequately explai…
Towards Modeling and Resolving Singular Parameter Spaces using Stratifolds
Pascal Mattia Esser, Frank Nielsen
When analyzing parametric statistical models, a useful approach consists in modeling geometrically the parameter space. However, even for very simple and commonly used hierarchical…
New Insights into Graph Convolutional Networks using Neural Tangent Kernels
Mahalakshmi Sabanayagam, Pascal Esser, Debarghya Ghoshdastidar
Graph Convolutional Networks (GCNs) have emerged as powerful tools for learning on network structured data. Although empirically successful, GCNs exhibit certain behaviour that has…