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Optimal Pruning for Neural Architectures using Fisher Information Distances

Via ArXiv cs.AI
Wednesday, Sep 16, 2026 · 4:00AM
Summary

arXiv:2609.16129v1 Announce Type: new Abstract: A new scheme for parameter pruning is introduced, derived from the differential-geometric distance in model space. Pruning a parameter sets its value to zero, representing a displacement of the model to the hypersurface on which that parameter vanishes

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