arXiv (All)
open access
Jumping up and down: Denoiser diffusion models for discrete ordinal data
Diffusion models are highly developed in continuous spaces for image and video domains. Recently, major advances have been made for discrete diffusion models for categorical data, specifically in the language domain. In contrast, diffusion models for discrete integer-valued data are less developed,
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
- Clock Diffusion: Efficient Semi-Autoregressive Continuous Diffusion Language Models#1155829
- Scaling Properties of Continuous Diffusion Spoken Language Models#138944
- Subliminal Clocks: Latent Time Modelling in Diffusion Language Models#1198963
- PixelDiT2: Representation-Grounded Pixel Diffusion Transformers#1051315
- DIVA: Exploiting Cross-Step Conditional Propagation for Visual Jailbreaks in Discrete Diffusion Vision-Language Models#972586
Record · ID 1187068
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