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Title: Besov Function Approximation and Binary Classification on Low-Dimensional Manifolds Using Convolutional Residual Networks
Most of existing statistical theories on deep neural networks have sample complexities cursed by the data dimension and therefore cannot well explain the empirical success of deep learning on high-dimensional data. To bridge this gap, we propose to exploit the low-dimensional structures of the real world datasets and establish theoretical guarantees of convolutional residual networks (ConvResNet) in terms of function approximation and statistical recovery for binary classification problem. Specifically, given the data lying on a ๐‘‘-dimensional manifold isometrically embedded in โ„^๐ท, we prove that if the network architecture is properly chosen, ConvResNets can (1) approximate Besov functions on manifolds with arbitrary accuracy, and (2) learn a classifier by minimizing the empirical logistic risk, which gives an excess risk in the order of ๐‘›โˆ’2s/(2s+d), where ๐‘  is a smoothness parameter. This implies that the sample complexity depends on the intrinsic dimension ๐‘‘, instead of the data dimension ๐ท. Our results demonstrate that ConvResNets are adaptive to low-dimensional structures of data sets.  more » « less
Award ID(s):
1818751
NSF-PAR ID:
10296154
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
International Conference on Machine Learning
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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