The article discusses the emergence of scientific machine learning, focusing on training problems with a large volume of smooth data. It introduces PETScML as a framework to bridge deep-learning software and conventional solvers. Empirical evidence shows the effectiveness of second-order solvers like L-BFGS and trust region methods in improving generalization errors for regression tasks.
Introduction
Background
Related Work
Contributions
Deep-learning Training
Software Architecture
Numerical Results
Further Questions
How can the findings from this study be applied to real-world applications?
What are potential drawbacks or limitations of using second-order solvers in practice?
How might advancements in hardware technology impact the efficiency of these solvers?
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by Stefano Zamp... pada arxiv.org 03-20-2024
https://arxiv.org/pdf/2403.12188.pdfPertanyaan yang Lebih Dalam