Equivariant Poincaré ResNets combine hyperbolic geometry with C4 and D4 group symmetries via specialized reshaping, permutations, and batch norm to reduce optimization space and speed convergence while staying inside the Poincaré ball.
Symmetry breaking and equivariant neural networks.arXiv preprint arXiv:2312.09016
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Equivariant neural networks support Goldstone-like modes enabling coherent information propagation across depth and recurrent iterations.
Adaptive canonicalization selects input canonical forms by maximizing network predictive confidence to yield continuous symmetry-preserving models with universal approximation for equivariant geometric networks.
citing papers explorer
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Group-Equivariant Poincar\'e Convolutional Networks
Equivariant Poincaré ResNets combine hyperbolic geometry with C4 and D4 group symmetries via specialized reshaping, permutations, and batch norm to reduce optimization space and speed convergence while staying inside the Poincaré ball.
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Spontaneous symmetry breaking and Goldstone modes for deep information propagation
Equivariant neural networks support Goldstone-like modes enabling coherent information propagation across depth and recurrent iterations.
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Adaptive Canonicalization with Application to Invariant Anisotropic Geometric Networks
Adaptive canonicalization selects input canonical forms by maximizing network predictive confidence to yield continuous symmetry-preserving models with universal approximation for equivariant geometric networks.