Summary of Towards Understanding How Attention Mechanism Works in Deep Learning, by Tianyu Ruan and Shihua Zhang
Towards understanding how attention mechanism works in deep learning
by Tianyu Ruan, Shihua Zhang
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); 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 investigates the underlying principles of attention mechanisms in neural networks, particularly those used in Transformers and graph attention networks. The authors analyze classic machine learning algorithms such as manifold learning, clustering, and supervised learning to identify key characteristics of similarity computation and information propagation. They then decompose the self-attention mechanism into a learnable pseudo-metric function and an information propagation process based on similarity computation. The paper proves that the self-attention mechanism converges to a drift-diffusion process under certain conditions and proposes a modified attention mechanism called metric-attention, which leverages metric learning to facilitate desired metrics. Experimental results show improved training efficiency, accuracy, and robustness compared to self-attention. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study explores how attention mechanisms in deep learning work by comparing them to traditional machine learning algorithms. The authors find that the self-attention mechanism is similar to classic methods like manifold learning and clustering, but operates more flexibly. They show that attention mechanisms can be broken down into two parts: a learnable pseudo-metric function and an information propagation process. This helps us understand how attention works and why it’s effective. The paper also proposes a new type of attention mechanism called metric-attention, which improves training efficiency and accuracy. |
Keywords
» Artificial intelligence » Attention » Clustering » Deep learning » Diffusion » Machine learning » Manifold learning » Self attention » Supervised
