https://github.com/brian-hepler-phd/spherical-cnn
Interactive exploration of equivariant neural networks on homogeneous spaces, with a focus on the sphere S² as SO(3)/SO(2). From Lecture 8 of the Lie groups course with Quantum Formalism
https://github.com/brian-hepler-phd/spherical-cnn
e3nn equivariant-neural-networks geometric-deep-learning homogeneous-spaces lie-groups machine-learning pytorch representation-theory scientific-computing spherical-cnn
Last synced: 9 months ago
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Interactive exploration of equivariant neural networks on homogeneous spaces, with a focus on the sphere S² as SO(3)/SO(2). From Lecture 8 of the Lie groups course with Quantum Formalism
- Host: GitHub
- URL: https://github.com/brian-hepler-phd/spherical-cnn
- Owner: brian-hepler-phd
- Created: 2025-05-06T12:41:30.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-05-06T12:53:53.000Z (12 months ago)
- Last Synced: 2025-06-18T14:41:22.097Z (10 months ago)
- Topics: e3nn, equivariant-neural-networks, geometric-deep-learning, homogeneous-spaces, lie-groups, machine-learning, pytorch, representation-theory, scientific-computing, spherical-cnn
- Language: Jupyter Notebook
- Homepage: https://bhepler.com
- Size: 733 KB
- Stars: 15
- Watchers: 1
- Forks: 2
- Open Issues: 0