aiqbench analysis
LAIR-Net introduces a novel architecture for tabular regression that leverages shallow learned anchors within randomized models to improve sensitivity bounds and performance on nonlinear targets. The study demonstrates that these gains are attributable to the anchor mechanism rather than increased model capacity or recursion, offering a more efficient approach for specific data regimes.
LAIR-Net: Leaky Alignment-Impulse Residual Networks for Tabular Regression
arXiv:2610.11538v1 Announce Type: cross 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 Net, the Leaky Alignment-Impulse Residual Network, which mixes a shallow learned anchor into each hidden state through a leaky residual transition. We derive a depth uniform bound on input-perturbation sensitivity and use controlled simulations to attribute gains over a randomized baseline to the anchor rather than recursion or added capacity. Benefits emerge when a nonlinear target structure is learnable at the available noise level and diminish for nearly linear targets or dominant noise. Across 23 benchmark datasets, LAIR-Net achieves the best average rank among eight randomized networks and twelve conventional models, with relative performance associated with the same nonlinear-structure and noise quantities identified in simulation.
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 Net, the Leaky Alignment-Impulse Residual Network, which mixes a shallow learned anchor into each hidden state through a leaky residual transition. We derive a depth uniform bound on input-perturbation sensitivity and use controlled simulations to attribute gains over a randomized baseline to the anchor rather than recursion or added capacity. Benefits emerge when a nonlinear target structure is learnable at the available noise level and diminish for nearly linear targets or dominant noise. Across 23 benchmark datasets, LAIR-Net achieves the best average rank among eight randomized networks and twelve conventional models, with relative performance associated with the same nonlinear-structure and noise quantities identified in simulation.
Machine Learning (stat.ML); Machine Learning (cs.LG)
arXiv:2610.11538 [stat.ML]
(or arXiv:2610.11538v1 [stat.ML] for this version)
https://doi.org/10.48550/arXiv.2610.11538 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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