Summary of Liere: Generalizing Rotary Position Encodings, by Sophie Ostmeier et al.
LieRE: Generalizing Rotary Position Encodings
by Sophie Ostmeier, Brian Axelrod, Michael E. Moseley, Akshay Chaudhari, Curtis Langlotz
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 proposes Lie Relative Encodings (LieRE), a novel method for transformer architectures that addresses limitations of Rotary Position Encoding (RoPE) in capturing relative position information. Unlike RoPE, which is constrained to one-dimensional sequence data, LieRE uses a learned, dense, high-dimensional rotation matrix of variable sparsity. This allows LieRE to achieve state-of-the-art performance on image datasets across 2D and 3D classification tasks, with a 2% relative improvement over baselines on 2D tasks and 1.5% on 3D tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to help transformers understand the relationships between different parts of an image. This is important because it can help computers learn more about images. The old way, called RoPE, had some limitations that made it hard to use with other types of data. The new method, LieRE, fixes these problems and performs well on image classification tasks. |
Keywords
» Artificial intelligence » Classification » Image classification » Transformer
