Summary of Looped Relu Mlps May Be All You Need As Practical Programmable Computers, by Yingyu Liang et al.
Looped ReLU MLPs May Be All You Need as Practical Programmable Computers
by Yingyu Liang, Zhizhou Sha, Zhenmei Shi, Zhao Song, Yufa Zhou
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Complexity (cs.CC)
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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 A recent study demonstrated that attention mechanisms are Turing complete, while another study showed that a looped 9-layer Transformer can function as a universal programmable computer. In contrast, multi-layer perceptrons with ReLU activation (ReLU-MLP) have been shown to be expressive, capable of approximating any function given an exponentially large number of hidden neurons. However, it remains unclear whether a ReLU-MLP can be made into a universal programmable computer using a practical number of weights. This work provides an affirmative answer, showing that a looped 23-layer ReLU-MLP is capable of performing basic operations and functioning as a programmable computer more efficiently than a looped Transformer. This highlights the potential for simple modules to have stronger expressive power than previously expected. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows that simple neural network modules can be very powerful. It’s like finding out that you don’t need a super complicated machine to do certain tasks, but instead, a simpler one can get the job done just as well. The researchers used a type of neural network called ReLU-MLP and showed that it can perform complex operations, like being able to follow instructions. This is important because it helps us understand how neural networks work and what they are capable of. |
Keywords
» Artificial intelligence » Attention » Neural network » Relu » Transformer
