Equivariant non-linear maps for neural networks on homogeneous spaces

• URL: http://arxiv.org/abs/2504.20974v1
• Date: Tue, 29 Apr 2025 17:42:56 GMT
• Title: Equivariant non-linear maps for neural networks on homogeneous spaces
• Authors: Elias Nyholm, Oscar Carlsson, Maurice Weiler, Daniel Persson,
• Abstract summary: We present a novel framework for non-linear equivariant neural network layers on homogeneous spaces.<n>We derive generalized steerability constraints that any such layer needs to satisfy.<n>We demonstrate how several common equivariant network architectures may be derived from our framework.
• Score: 8.944149301388551
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a novel framework for non-linear equivariant neural network layers on homogeneous spaces. The seminal work of Cohen et al. on equivariant $G$-CNNs on homogeneous spaces characterized the representation theory of such layers in the linear setting, finding that they are given by convolutions with kernels satisfying so-called steerability constraints. Motivated by the empirical success of non-linear layers, such as self-attention or input dependent kernels, we set out to generalize these insights to the non-linear setting. We derive generalized steerability constraints that any such layer needs to satisfy and prove the universality of our construction. The insights gained into the symmetry-constrained functional dependence of equivariant operators on feature maps and group elements informs the design of future equivariant neural network layers. We demonstrate how several common equivariant network architectures - $G$-CNNs, implicit steerable kernel networks, conventional and relative position embedded attention based transformers, and LieTransformers - may be derived from our framework.

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