Summary of Team Up Gbdts and Dnns: Advancing Efficient and Effective Tabular Prediction with Tree-hybrid Mlps, by Jiahuan Yan et al.
Team up GBDTs and DNNs: Advancing Efficient and Effective Tabular Prediction with Tree-hybrid MLPs
by Jiahuan Yan, Jintai Chen, Qianxing Wang, Danny Z. Chen, Jian Wu
First submitted to arxiv on: 13 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 research proposes a new framework for developing efficient and effective prediction algorithms for tabular data. The proposed framework, called T-MLP, combines the advantages of Gradient Boosted Decision Trees (GBDTs) and Deep Neural Networks (DNNs). The idea is rooted in an observation that deep learning offers a larger parameter space that can represent a well-performing GBDT model, yet current optimizers struggle to efficiently discover such optimal functionality. T-MLP combines key components from both models, including tensorized feature gates, pruning approaches, and vanilla back-propagation optimization. The proposed framework demonstrates competitive performance with extensively tuned DNNs and GBDTs on 88 tabular benchmarks, achieving compact model storage and reduced training duration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new algorithm for predicting data in tables. The goal is to create an efficient and effective way to make predictions that works well with different types of table data. Right now, two popular approaches are Gradient Boosted Decision Trees (GBDTs) and Deep Neural Networks (DNNs), but it’s hard to decide which one to use without a lot of testing. This paper combines the best parts of both GBDTs and DNNs into a new algorithm called T-MLP. It uses ideas from both approaches, like feature gates and pruning, to make predictions quickly and accurately. |
Keywords
* Artificial intelligence * Deep learning * Optimization * Pruning
