Summary of A Unified Fourier Slice Method to Derive Ridgelet Transform For a Variety Of Depth-2 Neural Networks, by Sho Sonoda et al.
A unified Fourier slice method to derive ridgelet transform for a variety of depth-2 neural networks
by Sho Sonoda, Isao Ishikawa, Masahiro Ikeda
First submitted to arxiv on: 25 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Functional Analysis (math.FA); 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 proposes a systematic method to derive ridgelet transforms for various modern neural network architectures. By using Fourier expressions, researchers can study the distribution of parameters in neural networks instead of individual neurons. The approach is applied to different types of networks, including those on finite fields, group convolutional networks, and pooling layers. This work enables a better understanding of how neural network parameters are distributed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how neural networks work by looking at the distribution of their parameters instead of individual neurons. It shows us a way to use special mathematical tools called Fourier expressions to study different types of neural networks, like those used in computers or image recognition. This can help us make better artificial intelligence models and improve our understanding of how they work. |
Keywords
* Artificial intelligence * Neural network
