Summary of Neural Networks Trained by Weight Permutation Are Universal Approximators, By Yongqiang Cai et al.
Neural Networks Trained by Weight Permutation are Universal Approximators
by Yongqiang Cai, Gaohang Chen, Zhonghua Qiao
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 provides theoretical guarantees for a novel permutation-based training method, which demonstrates excellent classification performance without modifying the exact weight values of ReLU networks. The proposed approach leverages the universal approximation property to guide neural networks in approximating one-dimensional continuous functions. Experimental results show that this method efficiently handles regression tasks with various initializations, highlighting its potential as an innovative tool for understanding network learning behavior. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper introduces a new way of training neural networks called permutation-based training. It’s a different approach from traditional methods and can achieve good results without changing the actual values of the network’s weights. The authors prove that this method works well for certain types of functions and show it can be used efficiently in regression tasks. They also found some interesting patterns about how the network learns using this new method. |
Keywords
* Artificial intelligence * Classification * Regression * Relu
