LAIR-Net: Leaky Alignment-Impulse Residual Networks for Tabular Regression

Deep randomized models fix hidden-layer parameters through random initialization and learn only closed-form readouts, typically adding depth by stacking random trans formations without target-aware control of hidden-state evolution.