Summary of Acceleration Of Grokking in Learning Arithmetic Operations Via Kolmogorov-arnold Representation, by Yeachan Park et al.
Acceleration of Grokking in Learning Arithmetic Operations via Kolmogorov-Arnold Representation
by Yeachan Park, Minseok Kim, Yeoneung Kim
First submitted to arxiv on: 26 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed methodologies aim to accelerate the grokking phenomenon, where test accuracy rapidly increases after a long period of overfitting. The study focuses on learning arithmetic binary operations via the transformer model and explores data augmentation strategies for commutative binary operations. By connecting the Kolmogorov-Arnold representation theorem to the transformer architecture, the authors reveal shared structures between KA representations associated with binary operations, enabling transfer learning mechanisms that expedite grokking. The approach is demonstrated through rigorous experiments on various arithmetic tasks, including composition of operations and a system of equations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make computers better at quickly understanding math problems after they’ve had some time to practice. It looks at how the transformer model, which is used for language translation, can be used to learn basic math like addition and multiplication. The authors think that by looking at how math works in a special way called the Kolmogorov-Arnold representation theorem, they can find ways to help computers learn math faster. They tested this idea with some experiments and found that it worked for learning simple math problems. |
Keywords
» Artificial intelligence » Data augmentation » Overfitting » Transfer learning » Transformer » Translation
