Improving Research in Machine Learning

Summarizing an analysis of components to improve reproducibility in ML research.

Photo by Scott Graham on Unsplash

I recently was tasked with reading a paper with MACHINE LEARNING as the primary topic. I chose to read the paper summarized below because I’ve used a variety of machine learning and similar artificial intelligence products in the past 6 months. While pursing the best solutions for our various business cases, my team has tested a number of systems trained with a variety of models. In our comparisons of established solutions, we needed a consistent method for comparison, using matching sample sets and a checklist of steps for comparison.

The paper summarized below, “Improving Reproducibility in Machine Learning Research,” approaches a similar problem in the world of research and development in machine learning. Machine learning research papers have often not been reproducible. The authors of this paper review the methods and results of a 2019 conference that implemented three components to attempt to improve the gap in reproducibility in machine learning research.

• TITLE: Improving Reproducibility in Machine Learning Research
• PUBLICATION: Journal of Machine Learning Research 22 (2021) 1–20
• LINK…