Summary of Binning As a Pretext Task: Improving Self-supervised Learning in Tabular Domains, by Kyungeun Lee et al.
Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domains
by Kyungeun Lee, Ye Seul Sim, Hye-Seung Cho, Moonjung Eo, Suhee Yoon, Sanghyu Yoon, Woohyung Lim
First submitted to arxiv on: 13 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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 proposed novel pretext task in the self-supervised learning framework for tabular domains leverages the classical binning method to reconstruct bin indices, either orders or classes. This approach provides an inductive bias for capturing irregular dependencies and mitigates feature heterogeneity by setting all features to category-type targets. The empirical investigations demonstrate several advantages of binning, including capturing irregular functions, compatibility with encoder architecture, standardizing features, grouping similar values, and providing ordering information. Comprehensive evaluations across diverse tabular datasets show that the method consistently improves tabular representation learning performance for a wide range of downstream tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to train deep networks for tabular data. It uses a technique called binning to help the network learn better representations. Binning is like taking a continuous value and turning it into a categorical value, which makes it easier for the network to understand the relationships between different features. The authors show that this approach works well across many different datasets and can be used as a pre-training task before fine-tuning the network on a specific problem. |
Keywords
» Artificial intelligence » Encoder » Fine tuning » Representation learning » Self supervised
