Summary of Random Projection Neural Networks Of Best Approximation: Convergence Theory and Practical Applications, by Gianluca Fabiani
Random Projection Neural Networks of Best Approximation: Convergence theory and practical applications
by Gianluca Fabiani
First submitted to arxiv on: 17 Feb 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 investigates the concept of Best Approximation for Feedforward Neural Networks (FNN) and explores their convergence properties through Random Projection Neural Networks (RPNNs). RPNNs have fixed internal weights and biases, offering computational efficiency. The study demonstrates that there exists a choice of external weights that exhibit an exponential convergence rate when approximating any infinitely differentiable function. The proposed method is tested across five benchmark function approximation problems, showing comparable performance to established methods like Legendre Polynomials. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well neural networks can approximate functions. It uses special types of neural networks called Random Projection Neural Networks (RPNNs) that are efficient because they have fixed internal weights and biases. The researchers show that there’s a way to choose the external weights in RPNNs so that they can accurately approximate any function with an exponential rate of improvement. They test this method on five different problems and find it works just as well as other methods. |
