Summary of Knowledge As a Breaking Of Ergodicity, by Yang He and Vassiliy Lubchenko
Knowledge as a Breaking of Ergodicity
by Yang He, Vassiliy Lubchenko
First submitted to arxiv on: 21 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Disordered Systems and Neural Networks (cond-mat.dis-nn); Computational Complexity (cs.CC); Machine Learning (stat.ML)
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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 thermodynamic potential is designed to guide the training of binary-degree-of-freedom generative models, which exhibit multiple minima upon description reduction. This mirrors the emergence of multiple minima in the free energy of the generative model itself. The potential’s non-represented configurations form a high-temperature phase separated from the training set by an extensive energy gap. Ergodicity breaking prevents escape into this near continuum, ensuring proper functionality but potentially limiting access to underrepresented patterns. Concurrently employing multiple generative models can serve as a remedy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to teach a computer to generate new pictures or sounds based on what it’s learned from a set of examples. A team of researchers has developed a special tool that helps the computer do this job by guiding its learning process. They found that when they “simplified” the computer’s description, it started behaving like multiple different models working together. This is important because it means the computer can learn and remember lots of different patterns, but might not be able to come up with new ideas if it didn’t see them in its training examples. |
Keywords
» Artificial intelligence » Generative model » Temperature
