Jarzynski reweighting, which estimates normalization constants from out-of-equilibrium paths, is shown to apply to a broad class of sampling kernels, including drift-based stochastic interpolants and RBM Gibbs sampling, with weights that vanish in the continuous-time limit.
Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Jarzynski Reweighting and Sampling Dynamics for Training Energy-Based Models: Theoretical Analysis of Different Transition Kernels
Jarzynski reweighting, which estimates normalization constants from out-of-equilibrium paths, is shown to apply to a broad class of sampling kernels, including drift-based stochastic interpolants and RBM Gibbs sampling, with weights that vanish in the continuous-time limit.