arXiv (All)
open access
Autonomous-Flow-Based Generation
We show that using autonomous-flow-based generation, one can universally approximate orientation-preserving diffeomorphisms defined on the cube by Neural ODEs with rate $\mathcal{O}(P^{-1/d})$ with $P$ parameters. On the other hand, we show that by using only a single autonomous flow, the class of N
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
- Approximating velocity fields with planted attractors via Neural-ODEs for classification purposes#299943
- Reward-Aware Trajectory Shaping for Few-step Visual Generation#927282
- Predicting Time-Dependent Flow Over Complex Geometries Using Operator Networks#1014575
- Embedding Non-Distortive Cancelable Face Template Generation#131212
- KoopCell: Koopman-Based Generative Model for Learning Single-Cell Dynamics from Distribution Snapshots#1118062
Record · ID 997453
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