Summary of Apar: Modeling Irregular Target Functions in Tabular Regression Via Arithmetic-aware Pre-training and Adaptive-regularized Fine-tuning, by Hong-wei Wu et al.
APAR: Modeling Irregular Target Functions in Tabular Regression via Arithmetic-Aware Pre-Training and Adaptive-Regularized Fine-Tuning
by Hong-Wei Wu, Wei-Yao Wang, Kuang-Da Wang, Wen-Chih Peng
First submitted to arxiv on: 14 Dec 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 The proposed Arithmetic-Aware Pre-training and Adaptive-Regularized Fine-tuning framework (APAR) aims to improve deep learning (DL) methods in tabular regression tasks. The framework addresses the challenges posed by irregular target functions in tabular data, which can lead to sensitive label changes with minor variations from features. APAR consists of a pre-training phase that introduces an arithmetic-aware pretext objective and a fine-tuning phase that employs a consistency-based adaptive regularization technique. This approach enables the model to fit irregular target functions while reducing overfitting. The framework outperforms existing GBDT-, supervised NN-, and pretrain-finetune NN-based methods in terms of RMSE, with an improvement of +9.43% to 20.37%. The study also investigates the effects of pre-training tasks, including the study of arithmetic operations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary APAR is a new way to improve deep learning models for tabular data. This type of data is used in many fields, such as finance and healthcare. The problem with tabular data is that it can be tricky for machines to learn from. APAR helps by making the model more aware of how numbers are related. It does this by introducing a special type of training that focuses on arithmetic operations. This approach has been shown to work well in practice, outperforming other methods in many cases. |
Keywords
» Artificial intelligence » Deep learning » Fine tuning » Overfitting » Regression » Regularization » Supervised
