Summary of Fourier Circuits in Neural Networks and Transformers: a Case Study Of Modular Arithmetic with Multiple Inputs, by Chenyang Li et al.
Fourier Circuits in Neural Networks and Transformers: A Case Study of Modular Arithmetic with Multiple Inputs
by Chenyang Li, Yingyu Liang, Zhenmei Shi, Zhao Song, Tianyi Zhou
First submitted to arxiv on: 12 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 As machine learning models continue to evolve, understanding how they represent internal features is crucial. A recent study explores why neural networks and Transformers adopt specific computational strategies. The research focuses on modular addition, a complex task involving multiple inputs. The authors analyze the features learned by stylized one-hidden layer neural networks and one-layer Transformers in addressing this task. They show that the principle of margin maximization shapes the features adopted by one-hidden layer neural networks. Additionally, they demonstrate that each hidden-layer neuron aligns with a specific Fourier spectrum, integral to solving modular addition problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about understanding how artificial intelligence models work. It looks at how two types of models, called neural networks and Transformers, solve complex math problems. The researchers found that these models learn different ways of doing things based on the problem they’re trying to solve. They also discovered that each part of the model is connected to a specific way of solving the problem. This helps us understand how these models work and can be useful for making them better. |
Keywords
* Artificial intelligence * Machine learning
