Summary of Higher Order Transformers: Efficient Attention Mechanism For Tensor Structured Data, by Soroush Omranpour et al.
Higher Order Transformers: Efficient Attention Mechanism for Tensor Structured Data
by Soroush Omranpour, Guillaume Rabusseau, Reihaneh Rabbany
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Transformers have revolutionized sequence modeling, but scaling them to higher-dimensional data remains a significant challenge due to the quadratic cost of attention mechanisms. This paper introduces Higher-Order Transformers (HOT), an innovative architecture designed to efficiently process higher-order tensors with more than two axes. To address computational challenges, HOT employs a novel Kronecker factorized attention mechanism that reduces attention costs to quadratic in each axis dimension, rather than total input size. Additionally, kernelized attention is used to further enhance efficiency, maintaining expressiveness while enabling scalable computation. The paper demonstrates the effectiveness of HOT on multivariate time series forecasting and 3D medical image classification tasks, showcasing competitive performance with significant computational efficiency gains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformers are super powerful tools for understanding sentences, but what if we wanted to use them for more complex data like images or videos? That’s exactly what this paper does. It introduces a new way to make transformers work with higher-dimensional data, which is really important because it can help us solve all sorts of problems in fields like medicine and finance. The idea is simple: instead of looking at the whole picture at once, we look at smaller parts of it and then put them back together. This makes the calculations much faster and more efficient. The paper shows that this new way works really well for forecasting stock prices and classifying medical images. |
Keywords
» Artificial intelligence » Attention » Image classification » Time series
