1 research outputs found
Extending Explainable Boosting Machines to Scientific Image Data
As the deployment of computer vision technology becomes increasingly common
in science, the need for explanations of the system and its output has become a
focus of great concern. Driven by the pressing need for interpretable models in
science, we propose the use of Explainable Boosting Machines (EBMs) for
scientific image data. Inspired by an important application underpinning the
development of quantum technologies, we apply EBMs to cold-atom soliton image
data tabularized using Gabor Wavelet Transform-based techniques that preserve
the spatial structure of the data. In doing so, we demonstrate the use of EBMs
for image data for the first time and show that our approach provides
explanations that are consistent with human intuition about the data.Comment: 7 pages, 2 figure