Summary of Elliptical Attention, by Stefan K. Nielsen et al.
Elliptical Attention
by Stefan K. Nielsen, Laziz U. Abdullaev, Rachel S.Y. Teo, Tan M. Nguyen
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 This paper proposes a novel attention mechanism called Elliptical Attention for transformer-based models. The approach addresses representation collapse and vulnerability to contaminated samples by using Mahalanobis distance metric for computing attention weights. This allows the model to focus on contextually relevant information, reducing reliance on specific features. The authors demonstrate the effectiveness of Elliptical Attention on various tasks, including object classification, image segmentation, and language modeling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to help computers understand relationships between things, like words or images. Right now, some computer models are very good at this, but they can get stuck in a pattern and not work well if the data is mixed up. The authors have found a solution to this problem by changing how the model looks at the information it’s given. This new way makes the model better at paying attention to important details, which helps it make more accurate predictions. |
Keywords
» Artificial intelligence » Attention » Classification » Image segmentation » Transformer
