2 citations · 2 across the 3 of their papers we have counts for
3 papers
A simple mean field model of feature learning
Niclas Göring, Chris Mingard, Yoonsoo Nam +1
Feature learning (FL), where neural networks adapt their internal representations during training, remains poorly understood. Using methods from statistical physics, we derive a tr…
Characterising the Inductive Biases of Neural Networks on Boolean Data
Chris Mingard, Lukas Seier, Niclas Göring +3
Deep neural networks are renowned for their ability to generalise well across diverse tasks, even when heavily overparameterized. Existing works offer only partial explanations (fo…
Bifurcations and loss jumps in RNN training
Lukas Eisenmann, Zahra Monfared, Niclas Alexander Göring +1
Recurrent neural networks (RNNs) are popular machine learning tools for modeling and forecasting sequential data and for inferring dynamical systems (DS) from observed time series.…