Summary of Estimating the Local Learning Coefficient at Scale, by Zach Furman et al.
Estimating the Local Learning Coefficient at Scale
by Zach Furman, Edmund Lau
First submitted to arxiv on: 6 Feb 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 In this paper, researchers introduce a new method for estimating the local learning coefficient (LLC), a measure of model complexity originally derived from Bayesian statistics and singular learning theory. The proposed approach enables accurate and self-consistent estimation of LLC for deep linear networks with up to 100 million parameters. The estimated LLC also exhibits rescaling invariance, matching theoretical predictions. This work extends the applicability of the LLC to modern deep learning architectures and datasets, potentially aiding model selection and optimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how complex machine learning models are. It introduces a new way to measure this complexity, called the local learning coefficient (LLC). The researchers show that this method works well for very big neural networks with millions of parameters. This is important because it can help us choose the right model for a problem and make them work better. |
Keywords
* Artificial intelligence * Deep learning * Machine learning * Optimization
