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This content will become publicly available on December 15, 2025

Title: How does Gradient Descent Learn Features---A Local Analysis for Regularized Two-Layer Neural Networks
The ability of learning useful features is one of the major advantages of neural networks. Although recent works show that neural network can operate in a neural tangent kernel (NTK) regime that does not allow feature learning, many works also demonstrate the potential for neural networks to go beyond NTK regime and perform feature learning. Recently, a line of work highlighted the feature learning capabilities of the early stages of gradient-based training. In this paper we consider another mechanism for feature learning via gradient descent through a local convergence analysis. We show that once the loss is below a certain threshold, gradient descent with a carefully regularized objective will capture ground-truth directions. We further strengthen this local convergence analysis by incorporating early-stage feature learning analysis. Our results demonstrate that feature learning not only happens at the initial gradient steps, but can also occur towards the end of training.  more » « less
Award ID(s):
2031849
PAR ID:
10627701
Author(s) / Creator(s):
;
Publisher / Repository:
NeurIPS 2024
Date Published:
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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