Summary of Learning Interpretable Hierarchical Dynamical Systems Models From Time Series Data, by Manuel Brenner et al.
Learning Interpretable Hierarchical Dynamical Systems Models from Time Series Data
by Manuel Brenner, Elias Weber, Georgia Koppe, Daniel Durstewitz
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Dynamical Systems (math.DS); Chaotic Dynamics (nlin.CD); Data Analysis, Statistics and Probability (physics.data-an)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles a crucial problem in science: creating generative models from time series data when observations come from multiple domains. While methods exist for reconstructing dynamics within a single domain, integrating data from different domains and leveraging it for generalization is an open question. The authors introduce a hierarchical framework that combines group-level information with single-domain characteristics, demonstrating its effectiveness on popular benchmarks and neuroscience/medical datasets. This approach not only reconstructs individual dynamics but also discovers low-dimensional feature spaces where similar datasets cluster. These features are surprisingly linearly related to control parameters governing the system’s dynamics. The paper also explores transfer learning and generalization to new parameter regimes, paving the way for foundational models in dynamical systems reconstruction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a model that can understand how things change over time when we have data from different types of events or processes. Right now, we’re good at making models based on data from one type of event, but we struggle to combine data from multiple types and use it to make predictions. The authors create a new way to do this by combining information from individual events with general patterns that apply across all the events. They test their method on real-world data and show that it works well for reconstructing how things change over time. |
Keywords
» Artificial intelligence » Generalization » Time series » Transfer learning
