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Gauge equivariant networks are neural networks that respect local symmetries (gauges) on manifolds, such as how vectors rotate when you change the local reference frame on a surface.
Manifold learning assumes high-dimensional data actually lies near a much lower-dimensional, smoothly curved surface embedded in a higher-dimensional space.