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

arXiv:2610.11538v1 Announce Type: new Abstract: 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. We propose LAIR Ne…

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  1. 2026-10-09 04:00 · arXiv stat.ML
    LAIR-Net: Leaky Alignment-Impulse Residual Networks for Tabular Regression

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