LAIR-Net: Leaky Alignment-Impulse Residual Networks for Tabular Regression
LAIR-Net introduces a network that combines a learned target-aware alignment anchor with leaky residual transitions to improve randomized models for tabular regression. It fixes hidden parameters through random initialization and learns only closed-form readouts, achieving superior performance compared to other models by leveraging the anchor's ability to capture nonlinear structures.