Berkeley Lab ‘Minimalist Machine Learning’ Algorithms Analyze Images From Very Little Data
CAMERA researchers develop highly efficient convolution neural networks tailored for analyzing experimental scientific images from limited training data
Mathematicians at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) 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. (more…)