arXiv (All)
open access
A Framework for the Systematic Review of ML Assets in AI Registries
Background: Modern software systems increasingly rely on Machine Learning (ML) assets (i.e., pre-trained models, datasets, benchmarks) for building, evaluating, and integrating ML-based systems. However, current exploration, selection and reuse practices of ML assets are not supported by systematic
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
- Bolo: Verified Model Hub for Next-Generation AI Databases#659090
- Bolo: Verified Model Hub for Next-Generation AI Databases#656954
- Bolo: Verified Model Hub for Next-Generation AI Databases#480147
- K-Bench: A Benchmark for LLM Unlearning in Agentic Deployments#1084438
- A Systematic Review on Hybrid AI Models Integrating Machine Learning and Federated Learning#382977
Record · ID 1233590
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