mlsanityData to 7 Oct 2026

Diffusion Density Estimators

arXiv 2410.06986 ↗First posted 9 Oct 2024cs.LG stat.MLcited by 2

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

We investigate the use of diffusion models as neural density estimators. The current approach to this problem involves converting the generative process to a smooth flow, known as the Probability Flow ODE. The log density at a given sample can be obtained by solving the ODE with a black-box solver. We introduce a new, highly parallelizable method that computes log densities without the need to solve a flow. Our approach is based on estimating a path integral by Monte Carlo, in a manner identical to the simulation-free training of diffusion models. We also study how different training parameters affect the accuracy of the density calculation, and offer insights into how these models can be made more scalable and efficient.

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 12 of its 26 references that are in the map. First posted on arXiv 9 Oct 2024.

Builds on

12 of 26 references.

Closest ancestors · 4

Same direction, newest first

  1. 2023Generative Diffusion From An Action Principle6 Oct
  2. 2021A Variational Perspective on Diffusion-Based Generative Models and Score Matching5 Jun
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equations26 Nov
  4. 2020Denoising Diffusion Probabilistic Models19 Jun

Other references · 8

  1. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  2. 2018Neural Ordinary Differential Equationscited by 3,733
  3. 2016Density estimation using Real NVPcited by 2,029
  4. 2017Masked Autoregressive Flow for Density Estimationcited by 680
  5. 2018FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Modelscited by 584
  6. 2023Your Diffusion Model is Secretly a Zero-Shot Classifiercited by 301
  7. 2020Interacting particle solutions of Fokker-Planck equations through gradient-log-density estimationcited by 87
  8. 2018Estimating the Spectral Density of Large Implicit Matricescited by 16

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025RNE: plug-and-play diffusion inference-time control and energy-based trainingcited by 16
  2. 2025Comparing Normalizing Flows with Kernel Density Estimation in Estimating Risk of Automated Driving Systemscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Diffusion-Model-Assisted Supervised Learning of Generative Models for Density Estimation
  2. 2025Diffusion Models: A Mathematical Introduction
  3. 2024A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models
  4. 2025Nonparametric estimation of a factorizable density using diffusion models
  5. 2024Non-Normal Diffusion Models
  6. 2024A Practical Diffusion Path for Sampling
  7. 2023Diffusion Models for Generative Artificial Intelligence: An Introduction for Applied Mathematicians
  8. 2023Diffusion Models With Learned Adaptive Noise