arXiv (All)
open access
Convergence of Denoising Diffusion Probabilistic Models: A Rényi Divergence Perspective
Denoising diffusion probabilistic models (DDPMs) have emerged as a powerful framework for generative modeling by transforming standard Gaussian inputs into outputs that approximate a target distribution. While DDPMs have been proved to converge in total variation, KL divergence, and Wasserstein dist
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Let It Go or Learn to Self-Correct: Continuous Diffusion for Constrained Discrete Tasks#986621
- PDMD: Projected Distribution Matching Distillation for Video Diffusion Models#1130413
- PDMD: Projected Distribution Matching Distillation for Video Diffusion Models#1108739
- Order-Optimal Sample Complexity of Rectified Flows#1232651
- Beyond Global Divergences: A Local-Mass Perspective on Bayesian Inference#310832
Record · ID 1389930
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive