7 citations · 14 across the 2 of their papers we have counts for
4 papers
Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed
Maria Refinetti, Sebastian Goldt, Florent Krzakala +1
A recent series of theoretical works showed that the dynamics of neural networks with a certain initialisation are well-captured by kernel methods. Concurrent empirical work demons…
Modelling the influence of data structure on learning in neural networks: the hidden manifold model
Sebastian Goldt, Marc Mézard, Florent Krzakala +1
Understanding the reasons for the success of deep neural networks trained using stochastic gradient-based methods is a key open problem for the nascent theory of deep learning. The…
Generalisation dynamics of online learning in over-parameterised neural networks
Sebastian Goldt, Madhu S. Advani, Andrew M. Saxe +2
Deep neural networks achieve stellar generalisation on a variety of problems, despite often being large enough to easily fit all their training data. Here we study the generalisati…
Thermodynamic efficiency of learning a rule in neural networks
Sebastian Goldt, Udo Seifert
Biological systems have to build models from their sensory data that allow them to efficiently process previously unseen inputs. Here, we study a neural network learning a linearly…