arXiv (All)
open access
Neural Sampling with Reweighted Normalizing Flows via the Wasserstein--Fisher--Rao JKO Scheme
We propose a neural algorithm for sampling from distributions specified by unnormalized Boltzmann densities. Our approach is based on the Jordan--Kinderlehrer--Otto scheme for the Kullback--Leibler divergence in the Wasserstein--Fisher--Rao geometry (WFR JKO scheme). Our contributions are twofold. F
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
- One-Step Generation via Riemannian Wasserstein Gradient Flows#1200105
- SIMANF: Sample Free Learning of Unnormalized Distributions via Simulated Annealing in Normalizing Flows#1116610
- High-dimensional density estimation with tensorizing flow#1141372
- A Gradient Flow Approach to Solving Inverse Problems with Latent Diffusion Models#1012055
- Faster Inference of Flow-Based Generative Models via Improved Data-Noise Coupling#1129199
Record · ID 1234645
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