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E(n)-equivariant neural networks are models whose outputs transform predictably when inputs are rotated, translated, or reflected in n-dimensional Euclidean space.
Persistent homology tracks how topological features (components, loops, voids) appear and disappear as you grow a scale parameter over a filtered simplicial complex.