Summary of Tensor Attention Training: Provably Efficient Learning Of Higher-order Transformers, by Yingyu Liang et al.
Tensor Attention Training: Provably Efficient Learning of Higher-order Transformers
by Yingyu Liang, Zhenmei Shi, Zhao Song, Yufa Zhou
First submitted to arxiv on: 26 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 presents a novel multi-view attention mechanism called Tensor Attention, which can capture high-order correlations among multiple modalities. However, its O(n^3) time complexity hinders its utilization in transformers. The authors prove that the backward gradient of tensor attention training can be computed in almost linear time (n^(1+o(1))) under certain assumptions, making higher-order transformer training feasible. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to help machines understand relationships between different types of data, like images and text. This method, called Tensor Attention, is very powerful but takes too long to use in some machine learning models. The authors figured out a way to make it faster, which could lead to more accurate predictions and better applications. |
Keywords
» Artificial intelligence » Attention » Machine learning » Transformer
