arXiv (All)
open access
Dynamic gain neuromodulation attenuates stability gaps during continual joint training
Recent work in continual learning has highlighted the stability gap -- a temporary performance drop on previously learned tasks when new ones are introduced. This phenomenon reflects a mismatch between rapid adaptation and strong retention at task boundaries, underscoring the need for optimization m
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
- Learning without Overwriting: Structural Knowledge Fusion for Continual Learning#1399983
- Realistic Continual Learning Approach using Pre-trained Models#1083966
- XAI ‐Guided Continual Learning: Rationale, Methods, and Future Directions#1358684
- Structured stabilization in recurrent neural circuits through inhibitory synaptic plasticity#359646
- Sparse Subspace-to-Expert Sharing for Task-Agnostic Continual Learning#1389173
Record · ID 1400764
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