Canonical Regularisation of Wide Feature-Learning Neural Networks
Wide neural networks in the feature-learning regime drive modern deep learning, and yet they remain far less studied than their kernel-regime counterparts. We consider a critical yet under-explored difference between these two regimes: the regulariser and prior implied by gradient flow training. This canonical regularisation property is well-studied in kernel regime networks -- of all the infinite global minima, gradient flow selects exactly the vanishing ridge solution -- and underpins the celebrated NN-GP correspondence, precisely allowing the modelling of noise during training. However, we prove ridge regularisation biases gradient flow in feature-learning regime networks, even in the ...
| Authors | George Whittle, Pranav Vaidhyanathan, Juliusz Ziomek et al. |
|---|---|
| Primary category | stat.ML |
| Published | 18 May 2026 |
| Last updated at source | 18 May 2026 |
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