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Title: Bounds for the smallest eigenvalue of the NTK for arbitrary spherical data of arbitrary dimension
Bounds on the smallest eigenvalue of the neural tangent kernel (NTK) are a key ingredient in the analysis of neural network optimization and memorization. How- ever, existing results require distributional assumptions on the data and are limited to a high-dimensional setting, where the input dimension d0 scales at least log- arithmically in the number of samples n. In this work we remove both of these requirements and instead provide bounds in terms of a measure of distance between data points: notably these bounds hold with high probability even when d0 is held constant versus n. We prove our results through a novel application of the hemisphere transform.  more » « less
Award ID(s):
2145630 2212520
PAR ID:
10591841
Author(s) / Creator(s):
; ;
Publisher / Repository:
38th Conference on Neural Information Processing Systems (NeurIPS 2024)
Date Published:
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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