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Deep Neural Networks Achieve Universality via Lindeberg Exchange Principle

Researchers have developed a new approach to understand the behavior of deep neural networks in their infinite-width limit. By applying a Lindeberg principle specifically adapted for deep neural networks, they can quantify the distance between a network and its Gaussian limit. This method involves systematically replacing weights in each layer with Gaussian random variables, providing general bounds under certain activation function conditions. AI

影响 Provides a new theoretical framework for understanding the behavior of deep neural networks at scale.

排序理由 This is a research paper published on arXiv detailing a new theoretical approach for analyzing deep neural networks.

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

Deep Neural Networks Achieve Universality via Lindeberg Exchange Principle

报道来源 [2]

  1. arXiv stat.ML TIER_1 English(EN) · Filippo Giovagnini, Sotirios Kotitsas, Marco Romito ·

    Universality in Deep Neural Networks: An approach via the Lindeberg exchange principle

    arXiv:2605.02771v1 Announce Type: cross Abstract: We consider the infinite-width limit of a fully connected deep neural network with general weights, and we prove quantitative general bounds on the $2$-Wasserstein distance between the network and its infinite-width Gaussian limit…

  2. arXiv stat.ML TIER_1 English(EN) · Marco Romito ·

    Universality in Deep Neural Networks: An approach via the Lindeberg exchange principle

    We consider the infinite-width limit of a fully connected deep neural network with general weights, and we prove quantitative general bounds on the $2$-Wasserstein distance between the network and its infinite-width Gaussian limit, under appropriate regularity assumptions on the …