arXiv (All)
open access
Repeatability is not recovery: Quantifying algorithmic stability and topic recovery in Latent Dirichlet Allocation
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
- Repeatability is not recovery: Quantifying algorithmic stability and topic recovery in Latent Dirichlet Allocation#633332
- Reusing Latent Speech Representations for Query-Conditioned Topic Localization in Transcripts#1036233
- UnlearningSoup: Is Repeated Tuning Necessary for Large Language Model Unlearning?#1132277
- Parameters vs. Context: TRACE Fine-Tuning for Robust Retrieval-Augmented Generation#1104536
- DynaKRAG: A Unified Framework for Learnable Evidence Control in Multi-Hop Retrieval-Augmented Generation#665839
Record · ID 635681
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