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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.LG2022
Origami in N dimensions: How feed-forward networks manufacture linear separability
Christian Keup, Moritz Helias
Neural networks can implement arbitrary functions. But, mechanistically, what are the tools at their disposal to construct the target? For classification tasks, the network must tr…