For sufficiently wide random MLPs, cumulant and Hermite approximations of layer-wise activation distributions yield expected outputs at lower computational cost than Monte Carlo sampling, with good performance on rare-event probabilities.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Estimating the expected output of wide random MLPs more efficiently than sampling
For sufficiently wide random MLPs, cumulant and Hermite approximations of layer-wise activation distributions yield expected outputs at lower computational cost than Monte Carlo sampling, with good performance on rare-event probabilities.