arXiv (All)
open access
Localized Diffusion Models
Diffusion models are state-of-the-art tools for various generative tasks. Yet training these models involves estimating high-dimensional score functions, a task that in principle suffers from the curse of dimensionality. It is therefore important to understand how low-dimensional structure in the ta
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
- Manifold-Aligned Generative Transport#986824
- First Learn, Then Memorize: The Spectral Bias of Diffusion Models#1129968
- Learned End-to-End Guidance Schedules for Diffusion Models#1156774
- Mechanism Shift During Post-training from Autoregressive to Masked Diffusion Language Models#798735
- Mechanism Shift During Post-training from Autoregressive to Masked Diffusion Language Models#797321
Record · ID 1063752
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