arXiv (All)
open access
Universality and Convergence of Generative Flows
Generative flows sample from an unnormalized target by training a flow to be balanced, and the training loss is the signal a practitioner watches. We ask what that signal is worth: whether a small loss certifies an accurate sampler, whether the loss can be driven to zero, and how fast gradient desce
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
- Tracing Generated Samples to Training-Data Clusters in Flow-Matching Models#924658
- Tracing Generated Samples to Training-Data Clusters in Flow-Matching Models#920424
- Beyond the Manifold Hypothesis: Hybrid Spectral Parameterizations for Flow Matching#1116763
- Precise: SDE-Consistent Stochastic Sampling for RL Post-Training of Flow-Matching Models#222618
- Prediction--Loss Alignment for Sampler--Robust Flow Matching Training#998105
Record · ID 1201503
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