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Though deep neural networks have gained enormous successes in various fields(e.g., computer vision) with supervised learning, they have so far been stilltrailing after the performances of GBDTs on tabular data. Delving into thistask, we determine that a judicious handling of feature interactions andfeature representation is crucial to the effectiveness of neural networks ontabular data. We develop a novel neural network called ExcelFormer, whichalternates in turn between two attention modules that shrewdly manipulatefeature interactions and feature representation updates, respectively. Abespoke training methodology is jointly introduced to facilitate modelperformances. Specifically, by initializing parameters with minuscule values,these attention modules are attenuated when the training begins, and theeffects of feature interactions and representation updates grow progressivelyup to optimum levels under the guidance of our proposed specific regularizationschemes Feat-Mix and Hidden-Mix as the training proceeds. Experiments on 28public tabular datasets show that our ExcelFormer approach is superior toextensively-tuned GBDTs, which is an unprecedented progress of deep neuralnetworks on supervised tabular learning.

ExcelFormer: A Neural Network Surpassing GBDTs on Tabular Data