4 papers · 1 filter
Versor: A Geometric Sequence Architecture
Truong Minh Huy, Edward Hirst
A novel sequence architecture is introduced, Versor, which uses Conformal Geometric Algebra (CGA) in place of traditional linear operations to achieve structural generalization and…
A Machine Learning Approach to the Nirenberg Problem
Gianfranco Cortés, Maria Esteban-Casadevall, Yueqing Feng +4
This work introduces the Nirenberg Neural Network: a numerical approach to the Nirenberg problem of prescribing Gaussian curvature on for metrics that are pointwise conformal…
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…
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…