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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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GrooveSquid.com Paper Summaries

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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 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.

Keywords

» Artificial intelligence