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Neural Network Approximation Method Uses Chinese Remainder Theorem for Explicit Bounds

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new method for neural network approximation that provides explicit parameter bounds related to approximation error. By utilizing the Chinese Remainder Theorem as a constructive encoding mechanism, they can construct fixed-architecture networks with defined parameter-error trade-offs. This approach offers quantitative, non-asymptotic characterizations for both Lipschitz continuous and Hölder-smooth functions, addressing a gap in prior work that lacked such explicit bounds. AI

IMPACT This research could lead to more efficient and predictable neural network designs by providing explicit bounds on parameter magnitudes relative to approximation error.

RANK_REASON The cluster contains an academic paper detailing a new theoretical approach to neural network approximation.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Neural Network Approximation Method Uses Chinese Remainder Theorem for Explicit Bounds

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The cluster contains an academic paper detailing a new theoretical approach to neural network approximation.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Feng-Lei Fan, Ze-Yu Li, Chen-Yu Wang, Jian-Jun Wang ·

    On Explicit Super-Expressive Approximation for Neural Networks

    arXiv:2607.06781v1 Announce Type: new Abstract: In this work, we investigate the fixed-architecture neural network approximation with explicit parameter bounds and elementary activations. While prior work demonstrated super-expressive approximation using fixed-size networks, they…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    On Explicit Super-Expressive Approximation for Neural Networks

    In this work, we investigate the fixed-architecture neural network approximation with explicit parameter bounds and elementary activations. While prior work demonstrated super-expressive approximation using fixed-size networks, they lack quantitative and non-asymptotic characteri…

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