Research map / Learning theory and algorithms / Matrix and tensor methods
Spectral Filtering for General Linear Dynamical Systems
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use spectral filtering with phase components and autoregressive prediction | General linear dynamical systems · Synthetic 10-dimensional LDS, Gaussian inputs | prediction error | Helped |
| Use spectral filtering with phase components and autoregressive prediction | General linear dynamical systems · Synthetic 10-dimensional LDS, block impulse inputs | prediction error | Helped |
Closest papers by meaning
- Learning Linear Dynamical Systems via Spectral Filtering (NeurIPS 2017 · similarity 0.94)
- SpectraLDS: Provable Distillation for Linear Dynamical Systems (NeurIPS 2025 · similarity 0.88)
- Efficient Spectral Control of Partially Observed Linear Dynamical Systems (NeurIPS 2025 · similarity 0.88)
- Identifying latent state transitions in non-linear dynamical systems (ICLR 2025 · similarity 0.87)
- Learning to Discover Iterative Spectral Algorithms (NeurIPS 2026 · similarity 0.86)
- A Memory Efficient Unified Algorithm for Online Learning of Linear Dynamical Systems (NeurIPS 2026 · similarity 0.86)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.