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cs.LG2026
Kernel Renormalization in Bayesian Deep Neural Networks: the Equivalent Wishart Ansatz in the Proportional Regime
Paolo Baglioni, Christian Keup, Vincenzo Zimbardo +4
The scaling limit where both the size of the training set and the width of a deep neural network grow at the same rate, the so-called proportional-width regime, has been in…
cs.LG2023★ 3 cited
Local Kernel Renormalization as a mechanism for feature learning in overparametrized Convolutional Neural Networks
R. Aiudi, R. Pacelli, A. Vezzani +2
Feature learning, or the ability of deep neural networks to automatically learn relevant features from raw data, underlies their exceptional capability to solve complex tasks. Howe…