Statistical knowledge and domain expertise are key to extract actionable insights out of data, yet such skills rarely coexist together. In Machine Learning, high-quality results are only attainable via mindful data preprocessing, hyperparameter tuning and model selection. Domain experts are often overwhelmed by such complexity, de-facto inhibiting a wider adoption of ML techniques in other fields. Existing libraries that claim to solve this problem, still require well-trained practitioners. Those frameworks involve heavy data preparation steps and are often too slow for interactive feedback from the user, severely limiting the scope of such systems.
In this paper we present Alpine Meadow, a first Interactive Automated Machine Learning tool. What makes our system unique is not only the focus on interactivity, but also the combined systemic and algorithmic design approach; on one hand we leverage ideas from query optimization, on the other we devise novel selection and pruning strategies combining cost-based Multi-Armed Bandits and Bayesian Optimization.
We evaluate our system on over 300 datasets and compare against other AutoML tools, including the current NIPS winner, as well as expert solutions. Not only is Alpine Meadow able to significantly outperform the other AutoML systems while --- in contrast to the other systems --- providing interactive latencies, but also outperforms in 80% of the cases expert solutions over data sets we have never seen before.
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This content will become publicly available on October 1, 2024
Dreaming of Data: Examining Data Augmentation for Machine Learning in Additive Manufacturing
The data generated during additive manufacturing (AM) practice can be used to train machine learning (ML) tools to reduce defects, optimize mechanical properties, or increase efficiency. In addition to the size of the repository, emerging research shows that other characteristics of the data also impact suitability of the data for AM-ML application. What should be done in cases for which the data in too small, too homogeneous, or otherwise insufficient? Data augmentation techniques present a solution, offering automated methods for increasing the quality of data. However, many of these techniques were developed for machine vision tasks, and hence their suitability for AM data has not been verified. In this study, several data augmentation techniques are applied to synthetic design repositories to characterize if and to what degree they enhance their performance as ML training sets. We discuss the comparative advantage of these data augmentation techniques across several canonical AM-ML tasks.
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- Award ID(s):
- 2309250
- NSF-PAR ID:
- 10496756
- Publisher / Repository:
- Solid Freeform Fabrication Symposium
- Date Published:
- Journal Name:
- 2023 International Solid Freeform Fabrication Symposium
- Format(s):
- Medium: X
- Location:
- Austin, TX, USA
- Sponsoring Org:
- National Science Foundation
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