- By:
- Stomps, Jordan R; Dayman, Kenneth J; Randolph, Christian C; Hite, Jason
- Journal Name:
- Journal of Radioanalytical and Nuclear Chemistry
- Page Number:
- 9073-9083
- Volume:
- 334
- Publication Date:
- March 12, 2026
- Conference Name:
- International Conference on Methods and Applications of Radioanalytical Chemistry (MARC)
- Conference Location:
- Kailua-Kona, Hawaii, United States of America
- Conference Sponsor:
- American Nuclear Society
- View DOI Listing:
- https://doi.org/10.1007/s10967-025-10548-7
Abstract
Cutting-edge machine learning methods often require large volumes of curated training data, precluding their use in national security problems with rare events in massive datasets. We present a method for incorporating abstract knowledge into models tailored for sparse data. A subject matter expert defines salient concepts using data examples, which are encoded in the model’s embedding space. Models are then trained to respect these concepts. This method enables knowledge injection, yielding effective models with limited labeled data and the ability to assess model sensitivity for subject matter expertise across the nonproliferation mission space, as demonstrated with Raman spectra analysis.
