arXiv (All)
open access
Simplex Diffusion Models
Diffusion models have revolutionized generative modeling for continuous data through the gradual refinement of a belief state. This iterative refinement has not yet carried over to discrete diffusion models, which discard uncertainty at intermediate steps through categorical sampling (information co
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
- Simplex Diffusion Models#1108779
- Twist, Don't Tilt: Trajectory-Exact Constrained Decoding for Masked Diffusion Models#1130263
- Twist, Don't Tilt: Trajectory-Exact Constrained Decoding for Masked Diffusion Models#1108772
- Learned End-to-End Guidance Schedules for Diffusion Models#1156774
- Control Variate Score Matching for Diffusion Models#820311
Record · ID 1130133
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