Summary of Towards Certification: a Complete Statistical Validation Pipeline For Supervised Learning in Industry, by Lucas Lacasa et al.
Towards certification: A complete statistical validation pipeline for supervised learning in industry
by Lucas Lacasa, Abel Pardo, Pablo Arbelo, Miguel Sánchez, Pablo Yeste, Noelia Bascones, Alejandro Martínez-Cava, Gonzalo Rubio, Ignacio Gómez, Eusebio Valero, Javier de Vicente
First submitted to arxiv on: 4 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Data Analysis, Statistics and Probability (physics.data-an)
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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 proposes a comprehensive validation pipeline for AI-based certification in the aerospace industry, integrating deep learning, optimization, and statistical methods. The pipeline consists of 10 steps, each combining concepts from machine learning, optimization, statistics, and adapting them to an industrial scenario. A realistic supervised problem is illustrated: predicting stress-related failure modes during airflight maneuvers using a large feature set. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a validation pipeline for AI-based certification in the aerospace industry, combining deep learning, optimization, and statistical methods. This helps ensure reliability and safety in aircraft design. The pipeline includes 10 steps, merging concepts from different disciplines to develop computationally efficient solutions. The application of this pipeline is demonstrated with a realistic problem: predicting stress-related failure modes during airflight. |
Keywords
» Artificial intelligence » Deep learning » Machine learning » Optimization » Supervised
