# 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 networks by addressing the following data separation problem: under what conditions can a random neural network make two classes X − , X + ⊂ R d (with positive distance) linearly…

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