arXiv (All)
open access
Expected flow networks in stochastic environments and two-player zero-sum games
Generative flow networks (GFlowNets) are sequential sampling models trained to match a given distribution. GFlowNets have been successfully applied to various structured object generation tasks, sampling a diverse set of high-reward objects quickly. We propose expected flow networks (EFlowNets), whi
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
- Expected flow networks in stochastic environments and two-player zero-sum games#797126
- Partial GFlowNet: Accelerating Convergence in Large State Spaces via Strategic Partitioning#999197
- GFlowState: Visualizing the Training of Generative Flow Networks Beyond the Reward#126505
- vidax: A Unified JAX Framework for Video Generative Models on Accelerator Meshes#1011778
- vidax: A Unified JAX Framework for Video Generative Models on Accelerator Meshes#965389
Record · ID 798539
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