Summary of Approximation Of Relation Functions and Attention Mechanisms, by Awni Altabaa et al.
Approximation of relation functions and attention mechanisms
by Awni Altabaa, John Lafferty
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 explores the approximation properties of inner products in neural networks, demonstrating their capabilities as universal approximators for various relation functions. Specifically, it shows that a multi-layer perceptron’s self- inner product is a universal approximator for symmetric positive-definite relations, while the inner product between two different multi-layer perceptrons can approximate asymmetric relations. The study provides bounds on the number of neurons required to achieve a given level of approximation accuracy. The results are applied to analyzing the attention mechanism in Transformers, revealing that any retrieval mechanism defined by an abstract preorder can be approximated by attention through its inner product relations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how neural networks work together to help machines learn about relationships between things. It shows that when a network is compared to itself or combined with another network, it’s really good at learning about certain kinds of relationships. The study figures out how many “building blocks” (called neurons) are needed for the network to get good at this task. Finally, the research applies these findings to understand how a popular machine learning model called Transformers works. |
Keywords
* Artificial intelligence * Attention * Machine learning
