arXiv (All)
open access
From Redundancy to Minimality: Fixed-Point-Guided Hierarchical Reduction of Learned Piecewise-Linear Dynamics
Understanding a nonlinear dynamical system from time series requires not only reproducing its trajectories, but also identifying a simple representation that preserves its essential dynamical structure. Almost-linear recurrent neural networks (AL-RNNs) are piecewise-linear RNNs in which only a subse
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
- Fluid dynamics reduction methods for temporal networks.#295269
- Continual Learning of Dynamical Systems in Recurrent Neural Networks through Recyclable Unit Gating#1142957
- Dynamics Creation through Neural Dynamical Transfer Learning#996564
- LTV-CTDNet: Compositional Turning Decomposition for Short-Term Turning-Movement Forecasting#1119087
- Hybridizing Structural Time Series With Dynamic Nonlinear Autoregressive Neural Networks To Improve Cryptocurrency Price Prediction#225746
Record · ID 1156641
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