3 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…
stat.ML2024
Proportional infinite-width infinite-depth limit for deep linear neural networks
Federico Bassetti, Lucia Ladelli, Pietro Rotondo
We study the distributional properties of linear neural networks with random parameters in the context of large networks, where the number of layers diverges in proportion to the n…