arXiv (All)
open access
Neural ODEs Meet Concurrent Learning: Stable Online Learning with Lyapunov Guarantees
Neural ODEs learn dynamics from trajectory losses, but their adjoint gradients lack the regressor-times-parameter-error structure on which Lyapunov analyses of online adaptation rest, so training on streaming data comes without stability guarantees. We show that this structure is in fact present: th
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
- Characterizing Replay Retention Under Dynamics Shift in Model-Based Reinforcement Learning#1011862
- Persistence-Augmented Neural Networks#2588
- Adaptive Online Learning for Nonparametric Regression#624861
- Training Dynamics and Induced Preconditioning of ReLU Policy Gradient for Scalar LQR#1115537
- Adaptive Self-Consistency: From Black-Box Sampling to Distribution-Valued Feedback#1143532
Record · ID 1116620
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