arXiv (All)
open access
High-Dimensional Simulation-Based Inference in Latent Spaces
Neural simulation-based inference (SBI) has been widely successful in inferring a relatively small number of interpretable parameters from potentially high-dimensional observations, such as images or time series. Accordingly, representation learning in SBI has focused almost exclusively on compressi
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
- Neural Network Representation Formats — Fraunhofer-Gesellschaft Zur Foerderung Der Angewandten Forschung E.V. (US20250384299A1)#607345
- Flow-JEPA: Flow Matching for Robust Latent Dynamics in JEPA World Models#793253
- Flow-JEPA: Flow Matching for Robust Latent Dynamics in JEPA World Models#789249
- Beyond Gaussian Worlds: Latent Geometry Matters for JEPAs#1036049
- Beyond Gaussian Worlds: Latent Geometry Matters for JEPAs#1006876
Record · ID 1141703
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