Summary of A Survey on Universal Approximation Theorems, by Midhun T Augustine
A Survey on Universal Approximation Theorems
by Midhun T Augustine
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 a comprehensive review of universal approximation theorems (UATs) for neural networks (NNs), focusing on their approximation capabilities. Starting with fundamental results like Taylor’s theorem, Fourier’s theorem, and Weierstrass approximation theorem, the authors provide a systematic overview of UATs, covering both theoretical and numerical aspects. The paper explores UATs for arbitrary width and depth, highlighting their significance in understanding NNs’ representation abilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well neural networks can copy or approximate other functions. It’s like trying to draw a picture using lots of tiny building blocks. The authors show that these networks can get really close to copying any function they’re given. They do this by looking at some famous math theorems and seeing how they apply to neural networks. This is important because it helps us understand how well neural networks can represent things, like pictures or sounds. |
