Corpus ID: 236772380

# The Separation Capacity of Random Neural Networks

@article{Dirksen2021TheSC,
title={The Separation Capacity of Random Neural Networks},
author={Sjoerd Dirksen and Martin Genzel and Laurent Jacques and Alexander Stollenwerk},
journal={ArXiv},
year={2021},
volume={abs/2108.00207}
}
Neural networks with random weights appear in a variety of machine learning applications, most prominently as the initialization of many deep learning algorithms and as a computationally cheap alternative to fully learned neural networks. In the present article we enhance the theoretical understanding of random neural nets by addressing the following data separation problem: under what conditions can a random neural network make two classes X−,X+ ⊂ Rd (with positive distance) linearly separable… Expand

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