Demystifying Science: What is Minimalist Machine Learning?

Mathematicians at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab), U.S., have developed a new approach to machine learning aimed at experimental imaging data. Rather than relying on the tens or hundreds of thousands of images used by typical machine learning methods, this new approach “learns” much more quickly and requires far fewer images. The technique is formally called the “Mixed-Scale Dense Convolution Neural Network (MS-D)”. It requires far fewer parameters than traditional methods, converges quickly, and has the ability to “learn” from a remarkably small training set. Their approach is already being used to extract biological structure from cell images, and is poised to provide a major new computational tool to analyse data across a wide range of research areas.