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Neural Network Training Through Matrix Factorization
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DOI：10.1007/978-3-032-03705-3_3.png)
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Abstract
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In this paper, we present a novel supervised learning algorithm for neural network training based on QR decomposition with Householder reflections. The core of our study outlines the fundamental mathematical principles underlying this approach. To validate its effectiveness and robustness, we provide a detailed analysis of benchmark experiments, demonstrating the advantages of our method.
Keywords:
Neural Network Training
Matrix Factorization
QR Decomposition
Householder Reflections
Supervised Learning
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24
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Cited Papers
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Accelerating Neural Network Training with FSGQR: A Scalable and High-Performance Alternative to Adam
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