Artificial Neural Networks Generated by Low Discrepancy Sequences

DOI10.1007/978-3-030-98319-2_15arXiv2103.03543OpenAlexW3135586433MaRDI QIDQ6154303

Unnamed Author, Alexander Keller

Publication date: 14 February 2024

Published in: Springer Proceedings in Mathematics & Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2103.03543

zbMATH Keywords

neural networksdeterministic algorithmslow discrepancy sequencessparsityparallel computer hardware

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Monte Carlo methods (65C05)

Cites Work

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• Monte Carlo and quasi-Monte Carlo methods in scientific computing. Proceedings of a conference at the University of Nevada, Las Vegas, Nevada, USA, June 23-25, 1994
• Pre-defined Sparsity for Low-Complexity Convolutional Neural Networks
• Parallel Quasi-Monte Carlo Integration by Partitioning Low Discrepancy Sequences
• Remark on algorithm 659
• On the distribution of points in a cube and the approximate evaluation of integrals
• Parallel Processing with the Perfect Shuffle

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