2 citations · 4 across the 6 of their papers we have counts for
6 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…
Feature learning is decoupled from generalization in high capacity neural networks
Niclas Alexander Göring, Charles London, Abdurrahman Hadi Erturk +3
Neural networks outperform kernel methods, sometimes by orders of magnitude, e.g. on staircase functions. This advantage stems from the ability of neural networks to learn features…
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…
Position: Solve Layerwise Linear Models First to Understand Neural Dynamical Phenomena (Neural Collapse, Emergence, Lazy/Rich Regime, and Grokking)
Yoonsoo Nam, Seok Hyeong Lee, Clementine C J Domine +5
In physics, complex systems are often simplified into minimal, solvable models that retain only the core principles. In machine learning, layerwise linear models (e.g., linear neur…
Out-of-Domain Generalization in Dynamical Systems Reconstruction
Niclas Göring, Florian Hess, Manuel Brenner +2
In science we are interested in finding the governing equations, the dynamical rules, underlying empirical phenomena. While traditionally scientific models are derived through cycl…
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.…