[Submitted on 14 Sep 2026]
Title:Optimal Pruning for Neural Architectures using Fisher Information Distances
View a PDF of the paper titled Optimal Pruning for Neural Architectures using Fisher Information Distances, by David S. Berman and 3 other authors
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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. The minimal distance from the unpruned model to this hypersurface is naturally computed via the geodesic distance in the model space as determined by the Fisher information metric. This distance determines the true change in the model, and its performance, under pruning. By analysing progressively more faithful approximations of this geodesic distance a natural hierarchy of optimality for pruning methods is determined. This starts with the traditional magnitude pruning, then develops into new more sophisticated and effective pruning schemes. The method is demonstrated for both fully-connected networks and vision transformers, on MNIST and CIFAR-10, over the complete $0$-$100\%$ pruning range and across five random seeds. It outperforms pruning by parameter magnitude and by the local Fisher information alone in every architecture and dataset combination considered, on both accuracy and the Matthews correlation coefficient. Additionally, analysis of different levels of geodesic approximation produces intermediate pruning schemes that are computationally efficient and maintain near-optimal performance. This geometric picture supplies not only a state-of-the-art pruning methodology for AI models, but also a verified and mathematically-motivated justification for pruning schemes.
Comments: 21 pages, 4 figures, 4 tables
Subjects:
Artificial Intelligence (cs.AI); Information Theory (cs.IT); Differential Geometry (math.DG)
Report number: QMUL-PH-26-32
Cite as: arXiv:2609.16129 [cs.AI]
(or arXiv:2609.16129v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2609.16129
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Edward Hirst [view email] [v1] Mon, 14 Sep 2026 18:00:03 UTC (1,116 KB)
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