Summary of How Big Is Big Data?, by Daniel T. Speckhard et al.
How big is Big Data?
by Daniel T. Speckhard, Tim Bechtel, Luca M. Ghiringhelli, Martin Kuban, Santiago Rigamonti, Claudia Draxl
First submitted to arxiv on: 18 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Materials Science (cond-mat.mtrl-sci); Machine Learning (cs.LG); Computational Physics (physics.comp-ph)
GrooveSquid.com Paper Summaries
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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 Medium Difficulty summary: This paper explores the concept of “big” in machine learning for materials-science problems, going beyond just data volume to consider quality, veracity, and infrastructure. The authors examine how models generalize to similar datasets, gather high-quality data from heterogeneous sources, and design feature sets and model complexity to achieve desired expressivity. They also investigate the infrastructure requirements needed to create large datasets and train models on them. This work highlights unique challenges in big data, motivating further research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about how machine learning works with lots of data, not just a little bit. It’s not just about having more data, but also making sure the data is good quality and accurate. The authors look at how models work with similar datasets, gather good data from different sources, design models to do certain things, and figure out what kind of computers are needed for big tasks. They find that working with lots of data has its own special challenges that need to be solved. |
Keywords
» Artificial intelligence » Machine learning
