SCORENF: Score-based Normalizing Flows for Sampling Unnormalized distributions
cs.LG, hep-lat, quant-ph
Submitted: 2025-10-24
Updated: 2026-09-26
Comments: \c{opyright} 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Journal ref: Proceedings of the 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 4336-4340, 2026
DOI: 10.1109/ICASSP55912.2026.11463153
License: http://creativecommons.org/licenses/by/4.0/
The gist: Unnormalized probability distributions are central to modeling complex physical systems across various scientific domains.
Terminology
Abstract
Unnormalized probability distributions are central to modeling complex physical systems across various scientific domains. Traditional sampling methods, such as Markov Chain Monte Carlo (MCMC), often suffer from slow convergence, critical slowing down, poor mode mixing, and high autocorrelation. In contrast, likelihood-based and adversarial machine learning models, though effective, are heavily data-driven, requiring large datasets and often encountering mode covering and mode collapse. In this work, we propose ScoreNF, a score-based learning framework built on the Normalizing Flow (NF) architecture, integrated with an Independent Metropolis-Hastings (IMH) module, enabling efficient and unbiased sampling from unnormalized target distributions. We show that ScoreNF maintains high performance even with small training ensembles, thereby reducing reliance on computationally expensive MCMC-generated training data. We also present a method for assessing mode-covering and mode-collapse behaviours. We validate our method on synthetic 2D distributions (MOG-4 and MOG-8) and the high-dimensional ϕ 4 lattice field theory distribution, demonstrating its effectiveness for sampling tasks.
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