arXiv (All)
open access
Flow-based neural networks for scalable high resolution imaging of VLBI observations
Bayesian sampling methods in very-long-baseline interferometry enable reliable imaging of astronomical sources with a record high angular resolution. However, even after the transition from classical MCMC methods to flow-based neural networks, they remain computationally expensive as the number of r
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
- PotentialEP: Backprop-Free Training of CIFAR-Scale Generative Models via Equilibrium Propagation#1325305
- From Axion Detection to Dark Matter Astronomy#1357760
- Seq-Flow: Efficient Probabilistic Forecasting with Self-Rollout Error Control#1234865
- Seq-Flow: Efficient Probabilistic Forecasting with Self-Rollout Error Control#1227324
- Learning Astrophysical Uncertainties in Dark Matter Direct Detection with Simulation-Based Inference#1202578
Record · ID 1490048
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