Papers › BNEM: A Boltzmann Sampler Based on Bootstrapped Noised Energy Matching

BNEM: A Boltzmann Sampler Based on Bootstrapped Noised Energy Matching

15 Sep 2024arXiv:2409.09787archive 2025-07-28

Ruikang Ouyang, Bo Qiang, José Miguel Hernández-Lobato

Developing an efficient sampler capable of generating independent and identically distributed (IID) samples from a Boltzmann distribution is a crucial challenge in scientific research, e.g. molecular dynamics. In this work, we intend to learn neural samplers given energy functions instead of data sampled from the Boltzmann distribution. By learning the energies of the noised data, we propose a diffusion-based sampler, Noised Energy Matching, which theoretically has lower variance and more complexity compared to related works. Furthermore, a novel bootstrapping technique is applied to NEM to balance between bias and variance. We evaluate NEM and BNEM on a 2-dimensional 40 Gaussian Mixture Model (GMM) and a 4-particle double-well potential (DW-4). The experimental results demonstrate that BNEM can achieve state-of-the-art performance while being more robust.

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