arXiv (All)
open access
Learning Under Forgetting: Statistical Support-Selective Retention in Stochastic Training Dynamics
Prior work has shown that neural networks exhibit implicit biases toward low-complexity structure (e.g., spectral bias), memorization dynamics, and compression-like effects during training, but a unified dynamical account of selective retention remains incomplete. We propose Repeated Reinforcement w
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
- Temporal expectation triggers competition in working memory that leads to forgetting#138350
- Temporal expectation triggers competition in working memory that leads to forgetting#134303
- Continual Learning via Self-Probe Gradients#1117102
- Information-Theoretic Decoupled Prompt Tuning for Continual Learning#1062157
- Continual learning for recurrent neural networks: An empirical evaluation#163427
Record · ID 1143369
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