  Steerable convolutional neural networks (CNNs) provide a general framework
for building neural networks equivariant to translations and transformations of
an origin-preserving group $G$, such as reflections and rotations. They rely on
standard convolutions with $G$-steerable kernels obtained by analytically
solving the group-specific equivariance constraint imposed onto the kernel
space. As the solution is tailored to a particular group $G$, implementing a
kernel basis does not generalize to other symmetry transformations,
complicating the development of general group equivariant models. We propose
using implicit neural representation via multi-layer perceptrons (MLPs) to
parameterize $G$-steerable kernels. The resulting framework offers a simple and
flexible way to implement Steerable CNNs and generalizes to any group $G$ for
which a $G$-equivariant MLP can be built. We prove the effectiveness of our
method on multiple tasks, including N-body simulations, point cloud
classification and molecular property prediction.
