AI RESEARCH
LassoFlexNet: Flexible Neural Architecture for Tabular Data
arXiv CS.LG
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ArXi:2603.20631v1 Announce Type: cross Despite their dominance in vision and language, deep neural networks often underperform relative to tree-based models on tabular data. To bridge this gap, we incorporate five key inductive biases into deep learning: robustness to irrelevant features, axis alignment, localized irregularities, feature heterogeneity, and