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Symmetric Learning is a torch-based machine learning library tailored to optimization problems featuring symmetry priors. It provides equivariant neural network modules, models, and utilities for leveraging group symmetries in data. Installation ————

pip install symm-learning

Key Features#

  • Neural Network Modules (nn): Equivariant layers including linear, convolutional, normalization, and attention modules that respect group symmetries.

  • Models (models): Ready-to-use architectures like equivariant MLPs, Transformers, and CNN encoders for time-series and structured data.

  • Linear Algebra (linalg): Utilities for symmetric vector spaces—least squares, invariant projections, and isotypic decomposition.

  • Statistics (stats): Functions for computing statistics (mean, variance, covariance) of symmetric random variables.

  • Representation Theory (representation_theory): Tools for working with group representations, homomorphism bases, and irreducible decompositions.

Citation#

If you use symm-learning in research, please cite:

@software{ordonez_apraez_symmetric_learning,
  author  = {Ordonez Apraez, Daniel Felipe},
  title   = {Symmetric Learning},
  year    = {2026},
  url     = {https://github.com/Danfoa/symmetric_learning}
}

License#

This project is released under the MIT License. See LICENSE in the repository root.

Resources#