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New Ansatz Predicts Bayesian Deep Neural Network Performance

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new approximate method to predict the generalization performance of Bayesian deep neural networks (MLPs) with fixed depth. The approach utilizes an equivalent Wishart Ansatz to model the fluctuations of hierarchical empirical kernels, enabling a large deviation analysis in the proportional-width regime. This framework simplifies the representation learning process in deep networks to a set of scalar order parameters and extends to convolutional architectures by identifying a local kernel renormalization mechanism. AI

IMPACT This research offers a new theoretical framework for understanding and predicting the behavior of deep neural networks, potentially aiding in their design and optimization.

RANK_REASON The cluster contains a research paper detailing a new theoretical approach for analyzing deep neural networks.

Read on arXiv stat.ML →

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

New Ansatz Predicts Bayesian Deep Neural Network Performance

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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Paolo Baglioni, Christian Keup, Vincenzo Zimbardo, Rosalba Pacelli, Alessandro Vezzani, Raffaella Burioni, Pietro Rotondo ·

    Kernel Renormalization in Bayesian Deep Neural Networks: the Equivalent Wishart Ansatz in the Proportional Regime

    arXiv:2605.29684v1 Announce Type: cross Abstract: The scaling limit where both the size of the training set $P$ and the width $N$ of a deep neural network grow at the same rate, the so-called proportional-width regime, has been intensely studied for shallow, single-hidden-layer n…

  2. arXiv stat.ML TIER_1 English(EN) · Pietro Rotondo ·

    Kernel Renormalization in Bayesian Deep Neural Networks: the Equivalent Wishart Ansatz in the Proportional Regime

    The scaling limit where both the size of the training set $P$ and the width $N$ of a deep neural network grow at the same rate, the so-called proportional-width regime, has been intensely studied for shallow, single-hidden-layer networks. However, extending these non-perturbative…

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