arXiv (All)
open access
Stochastic Engrams for Efficient Continual Learning
The ability to learn continuously in artificial neural networks (ANNs) is often limited by catastrophic forgetting, a phenomenon in which new knowledge becomes dominant. By taking mechanisms of memory encoding in neuroscience (i.e., engrams) as inspiration, we propose a novel approach that integrate
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
- BID-LoRA: A Parameter-Efficient Framework for Continual Learning and Unlearning#13103
- Overcoming catastrophic forgetting in neural networks#7398
- Learning Under Forgetting: Statistical Support-Selective Retention in Stochastic Training Dynamics#1143369
- SPACE-LoRA: Allocating Activation-Subspace Protection for Continual Learning#1128723
- Kathleen Remembers: Length-Invariant One-Shot Recall Without Attention#799582
Record · ID 1129064
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