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Deep ReLU Networks Achieve Minimax Optimal Learning Rates for Spectral Barron Functions

By PulseAugur Editorial · [1 sources] ·

Researchers have established new minimax rates for deep neural networks learning spectral Barron functions. The study demonstrates that these functions, which can be efficiently approximated by shallow networks, can also be approximated by deep ReLU networks with a rate dependent on the number of nonzero parameters. Furthermore, the research shows that deep ReLU networks can learn spectral Barron functions at a fast convergence rate, which is proven to be minimax optimal up to logarithmic factors. AI

IMPACT Establishes theoretical bounds for learning complex functions with deep networks, potentially informing future model architectures.

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

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Deep ReLU Networks Achieve Minimax Optimal Learning Rates for Spectral Barron Functions

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

  1. arXiv stat.ML TIER_1 English(EN) · Songqiu Ma, Yunfei Yang ·

    Minimax rates for learning spectral Barron functions by deep ReLU neural networks

    arXiv:2609.39020v1 Announce Type: new Abstract: We study how well deep neural networks approximate and learn spectral Barron functions. Recent studies have shown that these function classes can be efficiently approximated by shallow neural networks without suffering from the curs…

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