3 citations · 3 across the 2 of their papers we have counts for
4 papers · 1 filter
Non-Parametric Representation Learning with Kernels
Pascal Esser, Maximilian Fleissner, Debarghya Ghoshdastidar
Unsupervised and self-supervised representation learning has become popular in recent years for learning useful features from unlabelled data. Representation learning has been most…
Representation Learning Dynamics of Self-Supervised Models
Pascal Esser, Satyaki Mukherjee, Debarghya Ghoshdastidar
Self-Supervised Learning (SSL) is an important paradigm for learning representations from unlabelled data, and SSL with neural networks has been highly successful in practice. Howe…
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…