arXiv (OAI Expanded)
open access
Generative Modeling by Minimizing the Wasserstein-2 Loss
This paper develops a generative model by minimizing the second-order Wasserstein loss (the $W_2$ loss) through a distribution-dependent ordinary differential equation (ODE), whose dynamics involves the Kantorovich potential associated with the true data distribution and a current estimate of it. A
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
- Official Review · ICLR 2024#787221
- Learning Through Energy Refinement and Manifold Projection: A Cooperative EBM-AE Framework#1083410
- On the generalized Langevin equation and the Mori projection operator technique#163110
- Posterior sampling by source-space MCMC via prior-based few-step transport maps#1155969
- Adaptive Conformal Prediction for Image Regression Models with Application to an Inertial Confinement Fusion Emulator#1155470
Record · ID 1070658
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