3 papers
cond-mat.dis-nn2026
Dynamics of neural scaling laws in random feature regression with powerlaw-distributed kernel eigenvalues
Jakob Kramp, Javed Lindner, Moritz Helias
Training large neural networks exposes neural scaling laws for the generalization error, which points to a universal behavior across network architectures of learning in high dimen…
cond-mat.dis-nn2026
Lecture notes: From Gaussian processes to feature learning
Moritz Helias, Javed Lindner, Lars Schutzeichel +1
These lecture notes develop the theory of learning in deep and recurrent neuronal networks from the point of view of Bayesian inference. The aim is to enable the reader to understa…
cond-mat.dis-nn2025
From Kernels to Features: A Multi-Scale Adaptive Theory of Feature Learning
Noa Rubin, Kirsten Fischer, Javed Lindner +5
Feature learning in neural networks is crucial for their expressive power and inductive biases, motivating various theoretical approaches. Some approaches describe network behavior…