Summary of Nonuniform Random Feature Models Using Derivative Information, by Konstantin Pieper and Zezhong Zhang and Guannan Zhang
Nonuniform random feature models using derivative information
by Konstantin Pieper, Zezhong Zhang, Guannan Zhang
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Numerical Analysis (math.NA)
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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 research proposes novel neural network initialization techniques using nonuniform parameter distributions driven by derivative data. The approach is developed within the context of shallow neural networks for non-parametric regression tasks, and is shown to outperform traditional uniform random feature models. The paper addresses specific activation functions (Heaviside and ReLU) and their smooth approximations (sigmoid and softplus), drawing on recent insights into harmonic analysis and sparse representations of neural networks. By leveraging these findings, the authors derive densities that concentrate in regions of the parameter space suitable for modeling local function derivatives. This leads to simplified sampling methods that achieve performance close to optimal networks in various scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to start training neural networks by using special distributions of parameters based on how the network’s output changes when input values change. This approach is useful for regression tasks, where the goal is to make predictions based on patterns in the data. The authors show that their method can be better than traditional methods at starting the training process, especially with certain types of activation functions. They also explain why their method works by drawing on recent research into how neural networks work. |
Keywords
» Artificial intelligence » Neural network » Regression » Relu » Sigmoid
