Summary of Petscml: Second-order Solvers For Training Regression Problems in Scientific Machine Learning, by Stefano Zampini et al.
PETScML: Second-order solvers for training regression problems in Scientific Machine Learning
by Stefano Zampini, Umberto Zerbinati, George Turkiyyah, David Keyes
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Mathematical Software (cs.MS); Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a lightweight software framework that bridges the gap between deep-learning software and conventional solvers for unconstrained minimization. It leverages the Gauss-Newton approximation of the Hessian in a trust region method to improve generalization errors in regression tasks, demonstrating superior efficacy compared to adaptive first-order methods. The framework is built on top of the Portable and Extensible Toolkit for Scientific computation and can be used for various scientific machine-learning techniques and test cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new software tool that helps scientists use computers better. It combines two different types of computer programs: one for deep learning and another for solving mathematical problems. The tool is designed to work well with many kinds of data and algorithms, making it useful for lots of scientific tasks. Scientists can use this tool to improve their results and make better predictions. |
Keywords
* Artificial intelligence * Deep learning * Generalization * Machine learning * Regression
