Summary of State Space Models, Emergence, and Ergodicity: How Many Parameters Are Needed For Stable Predictions?, by Ingvar Ziemann et al.
State space models, emergence, and ergodicity: How many parameters are needed for stable predictions?
by Ingvar Ziemann, Nikolai Matni, George J. Pappas
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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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 A novel study explores the relationship between model complexity and task execution in simple theoretical models. Researchers investigate whether large language models’ emergent capabilities, such as multi-step reasoning, can be replicated in a straightforward framework using self-supervised learning. They demonstrate that the process of learning linear dynamical systems exhibits a phase transition, where tasks requiring substantial long-range correlation necessitate a specific critical number of model parameters. This phenomenon is akin to emergence. The study also examines the role of the learner’s parametrization and considers a simple version of a linear dynamical system with hidden state. Results show that no learner using a linear filter can successfully learn the random walk unless the filter length exceeds a certain threshold dependent on the effective memory length and horizon. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores how many parameters are needed for a model to perform a task. It’s like trying to figure out how big something needs to be before it can do something cool. The study shows that simple models, like ones used in language learning, have a special point where they suddenly start doing better at tasks that require remembering things from long ago. This is kind of like when you learn a new skill and suddenly you’re really good at it. |
Keywords
» Artificial intelligence » Self supervised
