Showing 2025Show all
3 papers · 1 filter
cs.LG2025
Approximate Gaussianity Beyond Initialisation in Neural Networks
Edward Hirst, Sanjaye Ramgoolam
Ensembles of neural network weight matrices are studied through the training process for the MNIST classification problem, testing the efficacy of matrix models for representing th…
cs.LG2025
Grokking vs. Learning: Same Features, Different Encodings
Dmitry Manning-Coe, Jacopo Gliozzi, Alexander G. Stapleton +4
Grokking typically achieves similar loss to ordinary, "steady", learning. We ask whether these different learning paths - grokking versus ordinary training - lead to fundamental di…
hep-th2025
AInstein: Numerical Einstein Metrics via Machine Learning
Edward Hirst, Tancredi Schettini Gherardini, Alexander G. Stapleton
A new semi-supervised machine learning package is introduced which successfully solves the Euclidean vacuum Einstein equations with a cosmological constant, without any symmetry as…