Summary of Fine-grained Attention in Hierarchical Transformers For Tabular Time-series, by Raphael Azorin et al.
Fine-grained Attention in Hierarchical Transformers for Tabular Time-series
by Raphael Azorin, Zied Ben Houidi, Massimo Gallo, Alessandro Finamore, Pietro Michiardi
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes a novel hierarchical model, Fieldy, to analyze tabular time-series data. This type of data is common in real-life systems, such as financial transactions or healthcare records, where rows are chronologically related. Existing models use attention mechanisms to encode and attend to rows or columns separately, but this approach has limitations. The proposed Fieldy model combines row-wise and column-wise attention at the field level, allowing it to learn patterns across separate rows or columns. The authors evaluate their proposal against state-of-the-art models on regression and classification tasks using public datasets and show that combining row-wise and column-wise attention improves performance without increasing model size. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to analyze data that changes over time, like stock prices or medical records. This type of data is important for making predictions and decisions. The current methods for analyzing this data are limited because they only look at individual rows or columns separately. The proposed Fieldy model can look at the relationships between fields across different rows or columns, which helps improve its accuracy. The authors tested their model against other state-of-the-art models on real-world datasets and found that it performs better without increasing the complexity of the model. |
Keywords
* Artificial intelligence * Attention * Classification * Regression * Time series
