Summary of Aspects Of Importance Sampling in Parameter Selection For Neural Networks Using Ridgelet Transform, by Hikaru Homma and Jun Ohkubo
Aspects of importance sampling in parameter selection for neural networks using ridgelet transform
by Hikaru Homma, Jun Ohkubo
First submitted to arxiv on: 26 Jul 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 A novel approach to neural network initialization is introduced, leveraging an oracle distribution derived from the ridgelet transform to obtain suitable initial parameters. This connection enables avoidance of traditional backpropagation learning processes in simple cases, where only linear regression is required. The study explores the implications of importance sampling and proposes extensions to parameter sampling methods. Experimental results are presented for one-dimensional and high-dimensional examples, suggesting that weight parameter magnitude may be more critical than intercept parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers find a new way to start training neural networks using a special kind of distribution. This helps them avoid the usual process of adjusting model parameters and instead uses linear regression. The study shows how this works for simple cases and proposes new ways to select initial parameters. Results are shared for both small and large datasets. |
Keywords
» Artificial intelligence » Backpropagation » Linear regression » Neural network
