2 papers
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…
cond-mat.dis-nn2024
Kernel shape renormalization explains output-output correlations in finite Bayesian one-hidden-layer networks
P. Baglioni, L. Giambagli, A. Vezzani +3
Finite-width one hidden layer networks with multiple neurons in the readout layer display non-trivial output-output correlations that vanish in the lazy-training infinite-width lim…