arXiv (All)
open access
Transfer Learning for Power Outage Detection Task with Limited Training Data
Early detection of power outages is crucial for maintaining a reliable power distribution system. This research investigates the use of transfer learning and language models in detecting outages with limited labeled data. By leveraging pretraining and transfer learning, models can generalize to unse
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
- Conditional Transfer from Controlled Pretraining Mixtures to Code#1248828
- Prediction-based attack detection and mitigation mechanism in power system.#138602
- Official Review · ICLR 2025#787158
- An Approximate Bayesian Deep Learning Approach for Uncertainty-aware Differential Emission Measure Estimates in the Solar Corona from the SDO#984751
- StocHastIc Early Leakage Detection System (SHIELDS) for Water Distribution Networks#1175130
Record · ID 1401769
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