Summary of Tensor Polynomial Additive Model, by Yang Chen et al.
Tensor Polynomial Additive Model
by Yang Chen, Ce Zhu, Jiani Liu, Yipeng Liu
First submitted to arxiv on: 5 Jun 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 This paper presents the Tensor Polynomial Addition Model (TPAM), a novel additive model designed for interpretable machine learning on high-order data. Traditional vectorization methods can disrupt the multidimensional structure of such data, leading to decreased accuracy and increased computational complexity. TPAM addresses these issues by using tensor representation to retain the original structure information. The model employs hierarchical and low-order symmetric tensor approximation for parameter compression, enabling it to capture complex feature interactions with fewer parameters while maintaining interpretability. TPAM’s transparency allows for transparent decision-making and meaningful feature value extraction. The authors demonstrate its effectiveness as a post-processing module for other interpretation models, introducing two variants for class activation maps. Experimental results show that TPAM can enhance accuracy by up to 30% and compression rate by up to 5 times while maintaining interpretability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you want to make predictions about complex data with lots of connections between different parts. This paper proposes a new way to do this called the Tensor Polynomial Addition Model (TPAM). The problem is that when we try to simplify this data, it can lose its important structure and become harder to work with. TPAM solves this by keeping the original structure and using clever math to reduce the number of calculations needed. This makes it faster and more accurate than other methods. It also helps us understand what’s going on in the data by letting us see which parts are most important. The authors tested TPAM on several datasets and found that it can make predictions up to 30% better than other methods while keeping things simple. |
Keywords
» Artificial intelligence » Machine learning » Vectorization
