Mixed Poisson diffusion models and application to posterior sampling
More precisely, we introduce and study a new mathematical framework, called Mixed Poisson Diffusion Models (MPDM), with noising dynamics driven by multivariate counting processes.
ProofPaper ↗
Key points
- Diffusion models are a powerful framework for learning complex probability distributions.
- While widely used diffusion models for continuous data rely on Gaussian noising processes, we instead develop diffusion models based on Poisson noise.
- The success of Gaussian diffusion models has led to their use as prior models in a growing number of posterior sampling methods for inverse problems.
- Building on our Poisson generative dynamics, we develop a natural framework for posterior sampling in Poisson inverse problems and derive several posterior sampling algorithms based on the proposed model.
Sources (1)
- [1]Mixed Poisson diffusion models and application to posterior samplingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 01:57 PM
More precisely, we introduce and study a new mathematical framework, called Mixed Poisson Diffusion Models (MPDM), with noising dynamics driven by multivariate counting processes.
Diffusion models are a powerful framework for learning complex probability distributions.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 14, 2026The Design Space of Tri-Modal Masked Diffusion Models
- Oct 14, 2026RepForcing: Representation-Space Initialization for Autoregressive Video Distillation
- Oct 14, 2026CommerceGen-Studio: An End-to-End Framework for Commercial-Grade Main-Image Generation
- Oct 14, 2026Glance Forcing: Making Bidirectional Diffusion Autoregressive at a Glance
- Oct 13, 2026Correction Training for Diffusion Models
- Oct 13, 2026Diff Diffusion: Predicting Edit Paths with Diffusion Models for Efficient Code Editing