Weight Decay and Neuron Condensation: A Three-Stage Analysis of Two-Layer ReLU Networks

Abstract:Weight decay is widely used as a regularization technique in neural network training, yet its role in neuron condensation (parameter direction alignment) remains unclear. Starting from a parameter initialization in the neural tangent kernel regime, we characterize training dynamics under weight decay through three stages: rapid fitting, amplitude compression, and neuron condensation. Using a two-layer ReLU network, we analyze a residual correlation field that governs both neuron amplitudes and directions. During rapid fitting, the residual approaches a quasi-static equilibrium maintained by weight decay while the tangent kernel remains nearly unchanged. In the early stage of amplitude compression, kernel decay amplifies the residual correlation field, whose isolated attracting extrema provide candidates for condensation directions. As neuron amplitudes stabilize, we bound the drift of attracting extrema and demonstrate contraction of neuron directions around them, leading to neuron condensation. This staged analysis provides a dynamical understanding of how weight decay promotes a condensed representation, beyond reducing parameter norms.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2610.04533 [cs.LG]

(or arXiv:2610.04533v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.04533 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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