REVIEW 1 cited by
The Generalization Error of the Minimum-norm Solutions for Over-parameterized Neural Networks
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We study the generalization properties of minimum-norm solutions for three over-parametrized machine learning models including the random feature model, the two-layer neural network model and the residual network model. We proved that for all three models, the generalization error for the minimum-norm solution is comparable to the Monte Carlo rate, up to some logarithmic terms, as long as the models are sufficiently over-parametrized.
Forward citations
Cited by 1 Pith paper
-
Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data
A per-sample confidence score derived from the pNML min-max regret is applied to linear regression and neural networks, and improves OOD detection, adversarial robustness, and active learning.
Discussion (0). Continue with ORCID to comment.