weight-space-learning

This paper investigates the role of parameter symmetry in weight-space perception, showing that symmetry scatter alone accounts for nearly the entire degradation in performance between shared and independently-fitted neural networks. The study uses SIRENs and large-scale experiments to argue that computational advantages may justify weight-space methods over informational equivalence to function access.