Deep ReLU networks achieve optimal generalization rates with gradient descent

Researchers have established optimal generalization rates for gradient descent in deep ReLU networks, a significant step beyond previous findings. The new work achieves rates comparable to the minimax optimal rates seen in kernel settings, overcoming limitations of earlier studies that yielded suboptimal rates or required exponential dependence on network depth. A key technical innovation involves controlling activation patterns near a reference model, leading to a sharper Rademacher complexity bound for deep ReLU networks trained with gradient descent. AI

IMPACT Establishes theoretical underpinnings for improved deep learning model generalization.

RANK_REASON Academic paper detailing theoretical advancements in deep learning generalization. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Gradient Descent
• Yuanfan Li

• paper
• model release

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