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.
Efficient training of energy-based models using jarzynski equality.Advances in Neural Information Processing Systems, 36, 2024
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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.