Pruning Deep Neural Networks via the Marchenko--Pastur Distribution
Summary
arXiv:2606.02608v1 Announce Type: new Abstract: We study a Marchenko--Pastur (MP) random-matrix approach to pruning deep neural networks with very small post-pruning fine-tuning budgets. The main practical contribution is accuracy retention under short calibration and fine-tuning schedules, rather than a long post-pruning reoptimization pipeline. The theory gives deterministic data-path certificates: if the removed component $R$ has small propagated logit effect $L_s \| R \psi_1(s) \|_\infty$, pruning decreases an elastic-net objective and preserves samples whose dense margin exceeds twice the perturbation.
Why It Matters
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Key Facts
- SectorIndustrial AI
- Market—
- ImpactLow (42/100)
- SignalResearch