Extending Explainable Boosting Machines to Scientific Image Data
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 21 of its 56 references that are in the map. First posted on arXiv 25 May 2023.
Builds on
21 of 56 references.
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2 papers.