A dual-subnet network with a learned pT-dependent weighting learns broken symmetries faster than unconstrained networks while avoiding the performance ceiling of exactly invariant networks.
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Learning Broken Symmetries with Approximate Invariance
A dual-subnet network with a learned pT-dependent weighting learns broken symmetries faster than unconstrained networks while avoiding the performance ceiling of exactly invariant networks.