arXiv (All)
open access
Understanding Latent-Dimension Scaling in Dynamical-System Learning through Spectral Reliability
In deep learning, approximation theory motivates increasing representation size. We ask whether this benefit extends to dynamics learning through autoregressive prediction. We analyze the learned time evolution through the eigenstructure of Koopman operators, using relative residuals to detect spuri
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
- Early Learning Shapes Later Directions Of Representation Change In Continual Learning#1130923
- Physics is the Best Teacher: Consistency Learning for Time-Invariant Operators of Chaotic Dynamics#1199382
- ChronoSSM: Training for Temporally Aware Representations in Autoregressive State Space Models#444599
- A ghost mechanism: An analytical model of abrupt learning in recurrent networks#144241
- Structure-preserving Fourier Neural Operators for Long-time Cahn-Hilliard Dynamics under Coarse Temporal Supervision#1144020
Record · ID 1249146
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