arXiv (All)
open access
It Just Takes Two: Scaling Amortized Inference to Large Sets
Neural posterior estimation has emerged as a powerful tool for amortized inference, with growing adoption across scientific and applied domains. In many of these applications, the conditioning variable is a set of observations whose elements depend not only on the target but also on unknown factors
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
- Learning Under Forgetting: Statistical Support-Selective Retention in Stochastic Training Dynamics#1143369
- Forty Shades of Blue: Quality-Diversity Alignment via Mode-Conditioned Reinforcement Learning#1084804
- BRACE: Anchored Bellman-Residual Correction for Stale Critics in Asynchronous RL#1110351
- Strait: Perceiving Priority and Interference in ML Inference Serving#146911
- The Sequential Price of Continual Learning#1077601
Record · ID 1131524
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