From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning

ArXi:2604.05635v1 Announce Type: new Numerical preprocessing remains an important component of tabular deep learning, where the representation of continuous features can strongly affect downstream performance. Although its importance is well established for classical statistical and machine learning models, the role of explicit numerical preprocessing in tabular deep learning remains less well understood. In this work, we study this question with a focus on spline-based numerical encodings.