Summary of Neural Scaling Laws Rooted in the Data Distribution, by Ari Brill
Neural Scaling Laws Rooted in the Data Distribution
by Ari Brill
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Disordered Systems and Neural Networks (cond-mat.dis-nn)
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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 mathematical model based on percolation theory is developed to describe natural learning tasks, which is found to unify and ground prior works on neural scaling laws. The proposed model exhibits two distinct criticality regimes, each yielding optimal power-law scaling laws with respect to increasing model or data size. These regimes are associated with previously proposed theories of neural scaling, suggesting a universal mechanism underlying the empirical neural scaling laws observed across various architectures, tasks, and datasets. To test this theory, regression models are trained on toy datasets derived from percolation theory simulations, demonstrating the potential for quantitatively predicting language model scaling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers found that deep learning models get better as they grow bigger or have more data, following a special pattern called a power law. They wanted to understand why this happens and developed a new mathematical model based on percolation theory to describe how natural learning tasks work. This model shows that there are two main ways that neural networks can improve with size or data: one way is by breaking down complex tasks into smaller, simpler ones, while the other way is by finding patterns in the data itself. By testing this idea with simple datasets, they showed that their theory can help predict how well language models will perform. |
Keywords
» Artificial intelligence » Deep learning » Language model » Regression » Scaling laws
