The Computational Complexity of ReLU Network Training Parameterized by Data Dimensionality

Understanding the computational complexity of training simple neural networks with rectified linear units (ReLUs) has recently been a subject of intensive research. Closing gaps and complementing results from the literature, we present several results on the parameterized complexity of training two-...

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Bibliographic Details

Published in:	arXiv.org
Main Authors:	Froese, Vincent, Hertrich, Christoph, Niedermeier, Rolf
Format:	Paper
Language:	English
Published:	Ithaca Cornell University Library, arXiv.org 23.08.2022
Subjects:	Algorithms Complexity Lower bounds Neural networks Parameterization Parameters Polynomials Training
ISSN:	2331-8422
Online Access:	Get full text
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