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Despite groundbreaking success in image and text learning, deep learning hasnot achieved significant improvements against traditional machine learning (ML)when it comes to tabular data. This performance gap underscores the need fordata-centric treatment and benchmarking of learning algorithms. Recently,attention and contrastive learning breakthroughs have shifted computer visionand natural language processing paradigms. However, the effectiveness of theseadvanced deep models on tabular data is sparsely studied using a few data setswith very large sample sizes, reporting mixed findings after benchmarkingagainst a limited number of baselines. We argue that the heterogeneity oftabular data sets and selective baselines in the literature can bias thebenchmarking outcomes. This article extensively evaluates state-of-the-artattention and contrastive learning methods on a wide selection of 28 tabulardata sets (14 easy and 14 hard-to-classify) against traditional deep andmachine learning. Our data-centric benchmarking demonstrates when traditionalML is preferred over deep learning and vice versa because no best learningmethod exists for all tabular data sets. Combining between-sample andbetween-feature attentions conquers the invincible traditional ML on tabulardata sets by a significant margin but fails on high dimensional data, wherecontrastive learning takes a robust lead. While a hybrid attention-contrastivelearning strategy mostly wins on hard-to-classify data sets, traditionalmethods are frequently superior on easy-to-classify data sets with presumablysimpler decision boundaries. To the best of our knowledge, this is the firstbenchmarking paper with statistical analyses of attention and contrastivelearning performances on a diverse selection of tabular data sets againsttraditional deep and machine learning baselines to facilitate further advancesin this field.

Attention versus Contrastive Learning of Tabular Data -- A Data-centric Benchmarking