对比NLP方法在表格深度学习中的效率与性能表现
On the Efficiency of NLP-Inspired Methods for Tabular Deep Learning
- 引入NLP模型思路提升表格数据建模能力
- 发现模型规模扩大导致计算效率下降
- 适合关注表格数据模型优化的研究者
近年来，表格深度学习（Tabular Deep Learning, DL）取得了显著进展，性能超越传统模型。随着自然语言处理（NLP）技术的引入，如基于语言模型的方法，表格DL模型的复杂度和规模持续增长。尽管表格数据通常不面临可扩展性问题，但模型规模的急剧膨胀引发了效率担忧。然而，效率在表格DL研究中仍相对被忽视。本文对最新表格DL创新进行批判性分析，重点关注性能与计算效率的平衡。代码已开源：https://github.com/basf/mamba-tabular。
原文摘要 · Abstract (English)
Recent advancements in tabular deep learning (DL) have led to substantial performance improvements, surpassing the capabilities of traditional models. With the adoption of techniques from natural language processing (NLP), such as language model-based approaches, DL models for tabular data have also grown in complexity and size. Although tabular datasets do not typically pose scalability issues, the escalating size of these models has raised efficiency concerns. Despite its importance, efficiency has been relatively underexplored in tabular DL research. This paper critically examines the latest innovations in tabular DL, with a dual focus on performance and computational efficiency. The source code is available at https://github.com/basf/mamba-tabular.
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