Summary of Expressive Power Of Relu and Step Networks Under Floating-point Operations, by Yeachan Park et al.
Expressive Power of ReLU and Step Networks under Floating-Point Operations
by Yeachan Park, Geonho Hwang, Wonyeol Lee, Sejun Park
First submitted to arxiv on: 26 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper investigates the fundamental limits of neural networks, a topic crucial for developing more robust AI models. The authors explore the expressive power of neural networks under more realistic conditions, considering floating-point numbers and operations used in practice. They show that certain types of neural networks can memorize any input/output pairs and approximate continuous functions within an arbitrary error margin. The results highlight the importance of understanding how neural networks operate in real-world scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well artificial intelligence (AI) models called neural networks work when we use computers to run them. Right now, AI is getting smarter and more powerful, but we don’t fully understand its limits. In this study, scientists explored what happens when AI models are used on computers that can only represent a small part of the many possible numbers and operations. They found that certain types of neural networks can remember any pair of inputs and outputs or approximate any continuous function. This is important because it helps us understand how AI works in real-life situations. |
