arXiv (All)
open access
AIGS: Adaptive Incremental Gating System for Online Representation Learning in Non-Stationary Data Streams
Real-time data streams in Web of Things (WoT) and edge computing environments often evolve through latent regime changes. For online representation learning under strict computational constraints, the central problem is resolving the stability-plasticity dilemma: keeping useful historical knowledge
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
- FEDERATED NEUROMORPHIC INTELLIGENCE: ADVANCING ROBUSTNESS, EFFICIENCY, AND CONTINUAL ADAPTATION IN EDGE ENVIRONMENTS#226453
- CLARE: Scalable Class-Incremental Continual Learning via a Sparsity-Based Framework#1123430
- CLARE: Scalable Class-Incremental Continual Learning via a Sparsity-Based Framework#919413
- Hold-Out Scoring for Efficient Gaussian DAG Learning#1187099
- Federated learning and edge computing-based collaborative detection system for IoT anomaly behavior#474086
Record · ID 1186975
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