Enhancing Accuracy of Deep Learning Algorithms by Training with Low-Discrepancy Sequences
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Author / Producer
Date
2021
Publication Type
Journal Article
ETH Bibliography
yes
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Abstract
We propose a supervised deep learning algorithm based on low-discrepancy sequences as the training set. By a combination of theoretical arguments and extensive numerical experiments we demonstrate that the proposed algorithm significantly outperforms standard deep learning algorithms that are based on randomly chosen training data for problems in moderately high dimensions. The proposed algorithm provides an efficient method for building inexpensive surrogates for many underlying maps in the context of scientific computing. © 2021, Society for Industrial and Applied Mathematics
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Publication status
published
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Book title
Journal / series
Volume
59 (3)
Pages / Article No.
1811 - 1834
Publisher
SIAM
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
deep learning; deep neural networks; low-discrepancy sequences; Quasi-Monte Carlo
Organisational unit
03851 - Mishra, Siddhartha / Mishra, Siddhartha
Notes
Funding
770880 - Computation and analysis of statistical solutions of fluid flow (EC)
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