Summary of Ai Competitions and Benchmarks: Dataset Development, by Romain Egele et al.
AI Competitions and Benchmarks: Dataset Development
by Romain Egele, Julio C. S. Jacques Junior, Jan N. van Rijn, Isabelle Guyon, Xavier Baró, Albert Clapés, Prasanna Balaprakash, Sergio Escalera, Thomas Moeslund, Jun Wan
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning paper proposes a comprehensive framework for developing datasets for practical use, addressing the common pitfalls of manual data preparation and potential risks in AI-based projects. The authors outline established methodological tools, drawing from their experience, to guide the development of datasets for machine learning applications. This includes tasks such as requirements gathering, design, implementation, evaluation, distribution, and maintenance. The framework also covers data collection, transformation, and quality evaluation. By providing a structured approach to dataset development, the paper aims to mitigate risks and improve the success rate of AI-based projects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning helps us make predictions, create new things, or find patterns in big data. But getting that data ready for use is tricky. It often takes a lot of manual work to prepare the data, which can be time-consuming and prone to errors. This paper shows how to develop datasets for machine learning in a way that’s reliable and efficient. The authors share their experience and outline the steps involved in creating high-quality datasets, from collecting and transforming data to evaluating its quality. By following this framework, we can make AI-based projects more successful and reduce the risk of mistakes. |
Keywords
» Artificial intelligence » Machine learning
