Summary of Residual Random Neural Networks, by M. Andrecut
Residual Random Neural Networks
by M. Andrecut
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 In this paper, researchers challenge a common assumption in neural networks that single-layer feedforward networks require many hidden neurons relative to the input dimensionality to achieve good classification accuracy. They show that with reasonably high-dimensional datasets, it is possible to obtain good results even when the number of hidden neurons matches the input dimensionality. The authors also develop an efficient iterative residual training method for these random neural networks and extend it to a least-squares kernel version. Furthermore, they propose an encryption method to protect both the data and the resulting network model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies simple single-layer neural networks with random weights that can be trained efficiently by solving a ridge-regression problem. The main finding is that these networks can still work well even when the number of hidden neurons is similar to the input dimensionality, as long as the dataset has a reasonable size. The researchers also provide an easy-to-use training method and show how it can be adapted for kernel-based versions of the model. Finally, they suggest a way to keep the network’s weights secret by encrypting both the data and the resulting model. |
Keywords
» Artificial intelligence » Classification » Regression
