17 citations · 17 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
Critical feature learning in deep neural networks
Kirsten Fischer, Javed Lindner, David Dahmen +3
A key property of neural networks driving their success is their ability to learn features from data. Understanding feature learning from a theoretical viewpoint is an emerging fie…
A theory of data variability in Neural Network Bayesian inference
Javed Lindner, David Dahmen, Michael Krämer +1
Bayesian inference and kernel methods are well established in machine learning. The neural network Gaussian process in particular provides a concept to investigate neural networks…
Robust and fast post-processing of single-shot spin qubit detection events with a neural network
Tom Struck, Javed Lindner, Arne Hollmann +4
Establishing low-error and fast detection methods for qubit readout is crucial for efficient quantum error correction. Here, we test neural networks to classify a collection of sin…