Summary of Boosting Gets Full Attention For Relational Learning, by Mathieu Guillame-bert and Richard Nock
Boosting gets full Attention for Relational Learning
by Mathieu Guillame-Bert, Richard Nock
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 proposes an attention mechanism for structured data that combines well with tree-based models in boosting. The approach blends tabular data into a single file, which is then used to train aggregated trees. Each tree learns from simple tabular models in a top-down manner, and the learned features are progressively crafted using attention and aggregation mechanisms. The method is competitive against state-of-the-art models containing both tree-based and neural nets-based models. Experiments demonstrate the effectiveness of this approach on simulated and real-world domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us deal with messy data that’s not just one big table. In reality, we often have many tables connected by rules. The old way to tackle this was using neural networks, but they’re not always the best choice. This new method brings together tree-based models and attention mechanisms to solve this problem. It works by learning from small tables first, then combining the knowledge to make bigger features that help us understand the data better. The results show that our approach is just as good as other top methods. |
Keywords
* Artificial intelligence * Attention * Boosting
