arXiv (All)
open access
An Empirical Investigation of Pre-Trained Deep Learning Model Reuse in the Scientific Process
Deep learning has achieved recognition for its impact within natural sciences, yet the prohibitive financial and technical cost of training models from scratch inhibit adoption. Following software engineering community guidance, natural scientists are reusing pre-trained deep learning models (PTMs)
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
- An Empirical Investigation of Pre-Trained Deep Learning Model Reuse in the Scientific Process#788717
- Econometrics with Pre-Trained Embeddings for Unstructured Data#1105111
- Explainability from Training with Applications to TCR-Epitope Prediction#1131196
- PPDL: A Real-world Industrial User Retention Ratio Forecasting Framework Integrating Physical Priors with Deep Learning#1083289
- RiPPLE: Cross-Space Performance Prediction from Early Training for Neural Architecture Search#1000368
Record · ID 792717
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