Summary of Scaling Laws in Linear Regression: Compute, Parameters, and Data, by Licong Lin et al.
Scaling Laws in Linear Regression: Compute, Parameters, and Data
by Licong Lin, Jingfeng Wu, Sham M. Kakade, Peter L. Bartlett, Jason D. Lee
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Statistics Theory (math.ST); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 new study sheds light on the relationship between large-scale deep learning models and their test errors. The research finds that as model sizes increase along with data sizes, the test error of the trained model improves polynomially. This is contrary to conventional wisdom, which suggests that increasing model size leads to an increase in variance errors. Instead, the study’s findings align with general neural scaling laws, which predict a monotonic improvement in performance with larger models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning models are getting bigger and better at recognizing patterns in data. Scientists have discovered that as these models get larger and more powerful, they become even more accurate at predicting what will happen next. This is surprising because we thought that making the model too big would make it overfit the data – like trying to fit too many puzzle pieces together. But instead, bigger models are actually better at finding the right answers. |
Keywords
» Artificial intelligence » Deep learning » Scaling laws
