Schug, D.
, Yerramreddy, S.
, Caruana, R.
, Greenberg, C.
and Zwolak, J.
(2023),
Extending Explainable Boosting Machines to Scientific Image Data, Proceedings of the Machine Learning and the Physical Sciences Workshop, NeurIPS 2023
, New Orleans, LA, US, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=936910
(Accessed April 27, 2024)
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Extending Explainable Boosting Machines to Scientific Image Data
Published
Author(s)
Daniel Schug, Sai Yerramreddy, Rich Caruana, ,
Abstract
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.Proceedings Title
Proceedings of the Machine Learning and the Physical Sciences Workshop, NeurIPS 2023
Conference Dates
December 10-16, 2023
Conference Location
New Orleans, LA, US
Conference Title
Workshop on Machine Learning and the Physical Sciences
Pub Type
Conferences
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Keywords
explainable boosting machines for image data, scientific data, explainable image processing, physics, Bose-Einstein Condensates
Citation
Created November 30, 2023