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Estimating the expected output of wide random MLPs more efficiently than sampling
Approximate layer-wise activation distributions via cumulants and Hermite expansions to estimate wide MLP expected outputs without sampling, reducing FLOPs versus Monte Carlo and improving rare-event estimates.
Atlanta Poster Session 3, Thu, Dec 10, 10:00 AM–1:00 PM, Hall C1 · Published 2026
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AI panel: 11 of 20 reviewers recommend it
lenient 4/5
medium 6/10
strict 1/5