SamAdams, an adaptive-stepsize Langevin sampler with an auxiliary moving-average control variable, achieves larger stable steps and better accuracy than fixed-step BAOAB on several benchmark and neural-network posterior sampling problems.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.CO 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
A Langevin sampling algorithm inspired by the Adam optimizer
SamAdams, an adaptive-stepsize Langevin sampler with an auxiliary moving-average control variable, achieves larger stable steps and better accuracy than fixed-step BAOAB on several benchmark and neural-network posterior sampling problems.