Denoising Diffusion Probabilistic Models
denoising-diffusion-probabilistic-models-1f1d84d0·3 events·first seen 28d agoAliases: Denoising Diffusion Probabilistic Models, denoising diffusion probabilistic model
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The Annotated Diffusion Model
A Hugging Face blog post providing a detailed, annotated walkthrough of diffusion models for image generation, likely covering the mathematical foundations and implementation details of denoising diffusion probabilistic models (DDPMs). The post serves as an educational deep-dive into the architecture and training process of diffusion-based generative models. Published in mid-2022, it coincides with the period of rapid growth in diffusion model adoption.
SDPM: Survival Diffusion Probabilistic Model for Continuous-Time Survival Analysis
The paper proposes SDPM, a generative model using denoising diffusion to estimate continuous-time survival (time-to-event) distributions without parametric hazard assumptions or time discretization. It is evaluated on ten real datasets against tree-based, boosting, and neural baselines, showing competitive C-index, AUC, and Brier score performance.
PTL-Diffusion: Diffusion framework with periodic terminal laws for manifold-aware generation
PTL-Diffusion is a new diffusion modeling framework that replaces the standard single Gaussian terminal distribution with a periodic family of Gaussian terminal laws, embedding phase structure directly into the forward noising dynamics rather than only in the denoising network. The authors derive closed-form forward marginals and reverse posteriors for a periodically forced Ornstein-Uhlenbeck process, enabling standard noise-prediction training. Experiments on torus, cylinder, and face datasets show improvements in manifold-level distributional matching over DDPM baselines. The work is a proof-of-concept motivating structured terminal reference laws as a direction for geometry-aware generative modeling.