arXiv (All)
open access
Improving Function Space Flow Matching with Kernel Optimal Transport
Generative models for function-valued data, such as time series and solutions of partial differential equations, must learn distributions over infinite-dimensional spaces. Functional Flow Matching (FFM) extends Flow Matching to this setting, learning a velocity field whose flow transports a Gaussian
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
- Improving Function Space Flow Matching with Kernel Optimal Transport#1122299
- Discriminative Flow Matching: Beyond Time-Conditioning in Generative Restoration via Flow-State Representations#927808
- Beyond the Manifold Hypothesis: Hybrid Spectral Parameterizations for Flow Matching#1116763
- Autoencoders in Function Space#799823
- GFlowNets and variational inference#798523
Record · ID 1142348
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