• Corpus ID: 1474026

# Breaking the Curse of Dimensionality with Convex Neural Networks

@article{Bach2017BreakingTC,
title={Breaking the Curse of Dimensionality with Convex Neural Networks},
author={Francis R. Bach},
journal={J. Mach. Learn. Res.},
year={2017},
volume={18},
pages={19:1-19:53}
}
• F. Bach
• Published 30 December 2014
• Computer Science
• J. Mach. Learn. Res.
We consider neural networks with a single hidden layer and non-decreasing homogeneous activa-tion functions like the rectified linear units. By letting the number of hidden units grow unbounded and using classical non-Euclidean regularization tools on the output weights, we provide a detailed theoretical analysis of their generalization performance, with a study of both the approximation and the estimation errors. We show in particular that they are adaptive to unknown underlying linear…
474 Citations

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