An Empirical Study of Symmetry-Induced Parameter Sharing as an Inductive Bias in Neural Networks
This study empirically investigates whether parameter sharing based on task-inherent symmetry can serve as an inductive bias in neural networks. Although standard Multi-Layer Perceptrons (MLPs) are highly expressive, they are also prone to learning unnecessary dependencies on input order or struc...
