Discovering Symmetries in Neural Network Parameter Spaces
This paper formalizes data-dependent parameter symmetries and characterize loss invariance and the group-action axioms through infinitesimal conditions, which provide objectives for jointly learning group generators and nonlinear action maps.
Bo Zhao, Nima Dehmamy, Robin Walters et al.
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