Noether: The More Things Change, the More Stay the Same

TL;DR

This paper systematically explores how symmetries in neural networks influence training dynamics, revealing conserved quantities and restrictions on gradient paths, thereby deepening understanding of neural network behavior and over-parametrization effects.

Contribution

It provides a formal analysis of symmetry in neural networks using Noether's theorem, linking symmetries to conserved quantities and restrictions on training paths.

Findings

Symmetries lead to boundedness of weights and balance equations.

Data augmentation induces momentum-like restrictions.

Time symmetry relates to the Neural Tangent Kernel.

Abstract

Symmetries have proven to be important ingredients in the analysis of neural networks. So far their use has mostly been implicit or seemingly coincidental. We undertake a systematic study of the role that symmetry plays. In particular, we clarify how symmetry interacts with the learning algorithm. The key ingredient in our study is played by Noether's celebrated theorem which, informally speaking, states that symmetry leads to conserved quantities (e.g., conservation of energy or conservation of momentum). In the realm of neural networks under gradient descent, model symmetries imply restrictions on the gradient path. E.g., we show that symmetry of activation functions leads to boundedness of weight matrices, for the specific case of linear activations it leads to balance equations of consecutive layers, data augmentation leads to gradient paths that have "momentum"-type restrictions, and time symmetry leads to a version of the Neural Tangent Kernel. Symmetry alone does not specify the optimization path, but the more symmetries are contained in the model the more restrictions are imposed on the path. Since symmetry also implies over-parametrization, this in effect implies that some part of this over-parametrization is cancelled out by the existence of the conserved quantities. Symmetry can therefore be thought of as one further important tool in understanding the performance of neural networks under gradient descent.