23 citations · 42 across the 4 of their papers we have counts for
4 papers
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…
Simple and statistically sound recommendations for analysing physical theories
Shehu S. AbdusSalam, Fruzsina J. Agocs, Benjamin C. Allanach +72
Physical theories that depend on many parameters or are tested against data from many different experiments pose unique challenges to statistical inference. Many models in particle…