Summary of Numerical Approximation Capacity Of Neural Networks with Bounded Parameters: Do Limits Exist, and How Can They Be Measured?, by Li Liu et al.
Numerical Approximation Capacity of Neural Networks with Bounded Parameters: Do Limits Exist, and How Can They Be Measured?
by Li Liu, Tengchao Yu, Heng Yong
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Universal Approximation Theorem suggests that neural networks with specific activation functions and parameter settings can theoretically approximate any function. However, in practical scenarios where parameters like weights and biases are constrained, this theorem’s applicability becomes uncertain. This paper investigates whether the approximation capacity of a bounded neural network remains universal or if it has a limit. The authors examine the theoretical implications and propose methods to measure the practical limitations of such networks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Neural networks can theoretically approximate any function with the right activation functions and parameter settings. But what happens when these parameters are limited? This paper looks at whether this limitation affects how well neural networks work or if it has a limit. The authors explore these questions and suggest ways to measure how well bounded neural networks perform. |
Keywords
» Artificial intelligence » Neural network
