Machine learning models, especially larger models that are used in for example image or text datasets, can be expensive to train. During development models are usually trained multiple times for example to optimize hyperparameters, which can result in a large carbon footprint. This project specifically focuses specifically on medical data. There are some recent efforts, for example by Selvan et …
Supervisors: Veronika Cheplygina
Semester: Fall 2023
Tags: machine learning, medical imaging, data analysis, resource consumption