Summary of Samsa: Efficient Transformer For Many Data Modalities, by Minh Lenhat et al.
SAMSA: Efficient Transformer for Many Data Modalities
by Minh Lenhat, Viet Anh Nguyen, Khoa Nguyen, Duong Duc Hieu, Dao Huu Hung, Truong Son Hy
First submitted to arxiv on: 10 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed SAMSA mechanism, a context-aware linear complexity self-attention approach, addresses the limitations of efficient transformers and enables foundational modeling on various data modalities. By introducing a differentiable sampling without replacement method, SAMSA can attend to the most important token set defined by the data, reducing computational costs during inference. The results demonstrate competitive or even state-of-the-art performance on multiple benchmarks while offering faster inference times compared to specialized models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SAMSA is a new way of doing self-attention in transformers that makes them more efficient and effective. It’s like a special filter that helps the model focus on the most important parts of the data, which makes it better at learning from different types of information. This can be useful for things like chatbots or language translation. |
Keywords
» Artificial intelligence » Inference » Self attention » Token » Translation
