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Title: Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels
We study the relative power of learning with gradient descent on differentiable models, such as neural networks, versus using the corresponding tangent kernels. We show that under certain conditions, gradient descent achieves small error only if a related tangent kernel method achieves a non-trivial advantage over random guessing (a.k.a. weak learning), though this advantage might be very small even when gradient descent can achieve arbitrarily high accuracy. Complementing this, we show that without these conditions, gradient descent can in fact learn with small error even when no kernel method, in particular using the tangent kernel, can achieve a non-trivial advantage over random guessing.  more » « less
Award ID(s):
1764032
NSF-PAR ID:
10286827
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Proceedings of Machine Learning Research
Volume:
139
ISSN:
2640-3498
Page Range / eLocation ID:
7379-7389
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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