Residual Pathway Priors for Soft Equivariance Constraints

Marc Anton Finzi, Gregory Benton, Andrew Gordon Wilson

Keywords: symmetry, equivariance, group equivariance, priors

Abstract: Models such as convolutional neural networks restrict the hypothesis space to a set of functions satisfying equivariance constraints, and improve generalization in problems by capturing relevant symmetries. However, symmetries are often only partially respected, preventing models with restriction biases from fitting the data. We introduce Residual Pathway Priors (RPPs) as a method for converting hard architectural constraints into soft priors, guiding models towards structured solutions while retaining the ability to capture additional complexity. RPPs are resilient to approximate or misspecified symmetries, and are as effective as fully constrained models even when symmetries are exact. We show that RPPs provide compelling performance on both model-free and model-based reinforcement learning problems, where contact forces and directional rewards violate the assumptions of equivariant networks. Finally, we demonstrate that RPPs have broad applicability, including dynamical systems, regression, and classification.

Code Of Conduct: I certify that all co-authors of this work have read and commit to adhering to the NeurIPS Statement on Ethics, Fairness, Inclusivity, and Code of Conduct.

TL;DR: We introduce Residual Pathway Priors for converting hard architectural constraints to soft priors, enabling exploitation of approximate symmetries.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/residual-pathway-priors-for-soft-equivariance/code)