Summary of An Elementary Proof Of a Universal Approximation Theorem, by Chris Monico
An elementary proof of a universal approximation theorem
by Chris Monico
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents an elementary proof of a universal approximation theorem for neural networks with three hidden layers and increasing, continuous, bounded activation functions. This result builds upon previous research, offering a more accessible approach to understanding the capabilities of these artificial intelligence models. The authors’ contribution is significant, as it demonstrates that no advanced machinery beyond undergraduate analysis is required to achieve this outcome. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how neural networks with three hidden layers can approximate any continuous function, even if they have increasing, bounded activation functions. This means that these artificial intelligence systems are very good at learning and mimicking patterns in data. The proof is simple and easy to understand, which makes it accessible to a wider range of people. |