Researchers have developed a new Preconditioned Gradient Descent (PGD) algorithm for training over-parameterized neural networks in nonparametric regression tasks. This method, combined with early stopping, achieves a sharp regression rate that surpasses existing nearly-optimal rates. The algorithm's effectiveness stems from a novel decomposition of the network output and the application of local Rademacher complexity theory to control function class complexity. AI
IMPACT This research offers theoretical advancements in training neural networks, potentially leading to more efficient and accurate regression models.
RANK_REASON Academic paper detailing a new algorithm and its theoretical guarantees. [lever_c_demoted from research: ic=1 ai=1.0]
- arXiv
- gradient descent
- Neural tangent kernel
- Preconditioned Gradient Descent
- reproducing kernel Hilbert space
- Yingzhen Yang
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
