Summary of Thermodynamic Limit in Learning Period Three, by Yuichiro Terasaki and Kohei Nakajima
Thermodynamic limit in learning period three
by Yuichiro Terasaki, Kohei Nakajima
First submitted to arxiv on: 12 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Adaptation and Self-Organizing Systems (nlin.AO); Chaotic Dynamics (nlin.CD)
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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 paper investigates whether it’s possible to generate various periodic orbits by training a random neural network using only three data points. The authors find that, indeed, this is feasible, and they demonstrate how almost all learned periods are unstable and each network has its unique attractors. These attractors can even be untrained ones, which leads to the emergence of attractors of all periods after learning. Additionally, the paper explores specific properties of certain networks, including singular behavior at the infinite scale of weights limit and symmetry in learning period three. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows that you can create different types of repeating patterns by training a special kind of computer program called a neural network using just three pieces of information. The researchers discovered that most of these learned patterns are unstable and each one is unique, sometimes even being patterns that weren’t trained in the first place. This helps explain how these patterns can change and develop over time. |
Keywords
» Artificial intelligence » Neural network
