Parameter Symmetry in Sine-Network Implicit Neural Representations (SIREN-GAP)Jul 2026 - Present
- Proved that neuron permutations, sign flips and π bias shifts generate the complete function-preserving symmetry group of generic sine networks with 1 and 2 hidden layers
- Showed with a Shapley-value factorial experiment that this group alone accounts for 79.1 of the 80.4 percentage points of MNIST accuracy lost when INRs start from independent rather than shared initializations
- Designed an exactly invariant weight-space classifier via a (cos b, sin b) bias encoding that closes 91.7% of the gap at equal parameter count, against 62.8% for the best alignment method
- Released the method as phasorkit, a pip-installable PyTorch library with invariant layers and an invariance certifier; manuscript in arXiv moderation