23 citations · 42 across the 4 of their papers we have counts for
3 papers · 1 filter
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…
Unified field theoretical approach to deep and recurrent neuronal networks
Kai Segadlo, Bastian Epping, Alexander van Meegen +3
Understanding capabilities and limitations of different network architectures is of fundamental importance to machine learning. Bayesian inference on Gaussian processes has proven…