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Found 4 papers, 1 papers with code
A Dynamical Model of Neural Scaling Laws
On a variety of tasks, the performance of neural networks predictably improves with training time, dataset size and model size across many orders of magnitude.
Feature-Learning Networks Are Consistent Across Widths At Realistic Scales
We call this the bias of narrower width.
The Onset of Variance-Limited Behavior for Networks in the Lazy and Rich Regimes
For small training set sizes $P$, the generalization error of wide neural networks is well-approximated by the error of an infinite width neural network (NN), either in the kernel or mean-field/feature-learning regime.
Neural Networks as Kernel Learners: The Silent Alignment Effect
Can neural networks in the rich feature learning regime learn a kernel machine with a data-dependent kernel?
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