Summary of Robust Fourier Neural Networks, by Halyun Jeong et al.
Robust Fourier Neural Networks
by Halyun Jeong, Jihun Han
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 new approach to improve the robustness of neural networks when training on noisy data. The authors build upon previous work using Fourier embedding to remove spectral bias and introduce a simple diagonal layer after the Fourier embedding layer to make the network more robust to measurement noise. This modification allows the network to learn sparse Fourier features, which can be beneficial in certain scenarios. The paper provides theoretical justifications for this approach, leveraging recent developments in diagonal networks and implicit regularization in neural networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this research aims to improve how well artificial intelligence models work when they’re trained on data that’s not entirely accurate. To do this, the scientists create a new way to make these models more robust by adding an extra layer of information after they use something called Fourier embedding. This helps the model learn and understand noisy patterns in the data better. |
Keywords
» Artificial intelligence » Embedding » Regularization
