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Researchers explore minimal width for p-adic ReLU neural networks

By PulseAugur Editorial · [1 sources] ·

A new research paper published on arXiv explores the theoretical underpinnings of $p$-adic neural networks. The study determines the minimal width required for these networks to achieve universal approximation properties for continuous functions within compact open subsets. The research specifically focuses on $p$-adic analogues of the ReLU activation function and considers various norms, including $L_q$ and $C_1$ norms. AI

IMPACT This theoretical work contributes to the understanding of neural network architectures and their approximation capabilities in non-standard mathematical frameworks.

RANK_REASON Academic paper published on arXiv detailing theoretical properties of neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

Researchers explore minimal width for p-adic ReLU neural networks

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Academic paper published on arXiv detailing theoretical properties of neural networks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · S\'andor Z. Kiss, Ambrus P\'al ·

    The minimal width of universal $p$-adic ReLU neural networks

    arXiv:2603.00064v2 Announce Type: replace-cross Abstract: We determine the minimal width of $p$-adic neural networks with the universal approximation property for continuous $\mathbb Q_p$-valued functions on compact open subsets with respect to the $L_q$ norms and the $C_1$ norm,…

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