mlsanityData to 7 Oct 2026

Diffusion models learn distributions generated by complex Langevin dynamics

arXiv 2412.01919 ↗First posted 2 Dec 2024hep-lat cs.LGcited by 2

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

The probability distribution effectively sampled by a complex Langevin process for theories with a sign problem is not known a priori and notoriously hard to understand. Diffusion models, a class of generative AI, can learn distributions from data. In this contribution, we explore the ability of diffusion models to learn the distributions created by a complex Langevin process.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 13 of its 44 references that are in the map. First posted on arXiv 2 Dec 2024.

Builds on

13 of 44 references.

Closest ancestors · 9

Same direction, newest first

  1. 2024On learning higher-order cumulants in diffusion models28 Oct
  2. 2024Understanding Diffusion Models by Feynman's Path Integral17 Mar
  3. 2023Generative Diffusion Models for Lattice Field Theory6 Nov
  4. 2023Diffusion Models as Stochastic Quantization in Lattice Field Theory29 Sep
  5. 2022Elucidating the Design Space of Diffusion-Based Generative Models1 Jun
  6. 2021High-Resolution Image Synthesis with Latent Diffusion Models20 Dec
  7. 2021Maximum Likelihood Training of Score-Based Diffusion Models22 Jan
  8. 2020Score-Based Generative Modeling through Stochastic Differential Equations26 Nov
  9. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 4

  1. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  2. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  3. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  4. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,170

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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approachcited by 18
  2. 2025Group-Equivariant Diffusion Models for Lattice Field Theorycited by 3

Similar, not cited

Close in wording, no citation either way

  1. 2025Combining complex Langevin dynamics with score-based and energy-based diffusion models
  2. 2023Diffusion Models for Generative Artificial Intelligence: An Introduction for Applied Mathematicians
  3. 2024An Overview of Diffusion Models: Applications, Guided Generation, Statistical Rates and Optimization
  4. 2025Diffusion Models: A Mathematical Introduction
  5. 2024GUD: Generation with Unified Diffusion
  6. 2025Non-asymptotic Analysis of Diffusion Annealed Langevin Monte Carlo for Generative Modelling
  7. 2024Breaking the Curse of Dimensionality: Diffusion Models Efficiently Learn Low-Dimensional Distributions
  8. 2025Information-Theoretic Proofs for Diffusion Sampling