Summary of Towards a Theory Of Model Distillation, by Enric Boix-adsera
Towards a theory of model distillation
by Enric Boix-Adsera
First submitted to arxiv on: 14 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
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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 This paper investigates the limits of model distillation in machine learning. Model distillation replaces a complex model with a simpler one that approximates the original behavior. Despite its practical applications, fundamental questions about how much models can be distilled, and the resources required to do so, remain unanswered. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Model distillation is a way to make complicated machine learning models simpler. This makes it easier to use these models in real-world situations. But, we don’t know very well how much simplification is possible or what kind of computers and data are needed to simplify the models. |
Keywords
* Artificial intelligence * Distillation * Machine learning
