Summary of Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling, by Lechao Xiao
Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling
by Lechao Xiao
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The remarkable success of large language pretraining and the discovery of scaling laws signify a paradigm shift in machine learning. Notably, the primary objective has evolved from minimizing generalization error to reducing approximation error, and the most effective strategy has transitioned from regularization (in a broad sense) to scaling up models. This raises a critical question: how can we leverage these insights to develop more accurate and efficient language processing models? To address this challenge, researchers have proposed innovative approaches that build upon the success of pretraining and scaling laws. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The goal is to create better models for understanding human language by using bigger training datasets. This means finding ways to make computers understand what we’re saying better. It’s like asking a friend who’s really good at speaking English to help you learn another language, but instead it’s about teaching computers to be more accurate with words and sentences. |
Keywords
» Artificial intelligence » Generalization » Machine learning » Pretraining » Regularization » Scaling laws
