Summary of On the Choice Of the Non-trainable Internal Weights in Random Feature Maps, by Pinak Mandal et al.
On the choice of the non-trainable internal weights in random feature maps
by Pinak Mandal, Georg A. Gottwald
First submitted to arxiv on: 7 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Analysis, Statistics and Probability (physics.data-an); Methodology (stat.ME); 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 The paper presents a machine learning architecture called random feature maps, which can be viewed as a single-layer feedforward network with fixed internal weights and learned outer weights via linear regression. The choice of internal weights significantly impacts the accuracy of random feature maps, and the authors propose a computationally cheap hit-and-run algorithm to select good internal weights for forecasting tasks. They show that the number of good features is the key factor controlling forecasting skill in random feature maps, acting as an effective feature dimension. The authors also compare random feature maps with single-layer feedforward neural networks learned using gradient descent, finding superior forecasting capabilities at a much lower computational cost. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at a special kind of machine learning called “random feature maps.” It’s like a shortcut to making predictions about what will happen next in a system. The important part is choosing the right settings for this method, and the authors came up with an easy way to do that. They found that how well it works depends on how many good features you use. This method is really fast and can make better predictions than some other ways of doing things. |
Keywords
» Artificial intelligence » Feedforward network » Gradient descent » Linear regression » Machine learning
