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Found 5 papers, 3 papers with code
Learning functions on symmetric matrices and point clouds via lightweight invariant features
In this work, we present a mathematical formulation for machine learning of (1) functions on symmetric matrices that are invariant with respect to the action of permutations by conjugation, and (2) functions on point clouds that are invariant with respect to rotations, reflections, and permutations of the points.
GeometricImageNet: Extending convolutional neural networks to vector and tensor images
We use representation theory to quantify the dimension of the space of equivariant polynomial functions on 2-dimensional vector images.
Machine learning and invariant theory
Inspired by constraints from physical law, equivariant machine learning restricts the learning to a hypothesis class where all the functions are equivariant with respect to some group action.
Dimensionless machine learning: Imposing exact units equivariance
Units equivariance (or units covariance) is the exact symmetry that follows from the requirement that relationships among measured quantities of physics relevance must obey self-consistent dimensional scalings.
Scalars are universal: Equivariant machine learning, structured like classical physics
There has been enormous progress in the last few years in designing neural networks that respect the fundamental symmetries and coordinate freedoms of physical law.
