Summary of Lower Bounds on Transformers with Infinite Precision, by Alexander Kozachinskiy
Lower bounds on transformers with infinite precision
by Alexander Kozachinskiy
First submitted to arxiv on: 28 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper presents a novel approach to proving lower bounds on one-layer softmax transformers with infinite precision using the VC dimension technique. The authors demonstrate their method’s effectiveness for two tasks: function composition, which has been studied before, and the SUM_2 task. By leveraging these results, the paper contributes to our understanding of the capabilities and limitations of deep learning models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to measure how good one-layer softmax transformers are at doing certain tasks. It uses a special math technique called VC dimension to show that these transformers can’t do as well as we think they can on some tasks. The authors test their method on two different problems and show that it works. |
Keywords
» Artificial intelligence » Deep learning » Precision » Softmax
