Weight decay optimization in neural networks requires overparametrization and specific initialization

A new research paper explores the theoretical underpinnings of neural network optimization when using weight decay. The study demonstrates that a benign loss landscape, free from spurious local minima, is achieved in two-layer ReLU networks under significant overparametrization. This benign landscape is primarily relevant in a large initialization regime, as smaller initializations can still lead to convergence in spurious local minima despite the overall landscape's favorable properties. AI

IMPACT Provides theoretical insights into optimizing neural networks, potentially guiding future model development and training strategies.

RANK_REASON Academic paper detailing theoretical findings on neural network optimization. [lever_c_demoted from research: ic=1 ai=1.0]

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• Etienne Boursier

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