In the linear-width regime, the second GD step yields a spiked random matrix whose number of outliers is floor(alpha2 / (1/2 - alpha1)), and batch reuse enables learning directions with information exponent greater than one under suitable alpha scalings.
Journal of Functional Analysis , volume=
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ML 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent
In the linear-width regime, the second GD step yields a spiked random matrix whose number of outliers is floor(alpha2 / (1/2 - alpha1)), and batch reuse enables learning directions with information exponent greater than one under suitable alpha scalings.