Model editing for distribution shifts in uranium oxide morphological analysis
<|reference_start|>Model editing for distribution shifts in uranium oxide morphological analysis: Deep learning still struggles with certain kinds of scientific data. Notably, pretraining data may not provide coverage of relevant distribution shifts (e.g., shifts induced via the use of different measurement instruments). We consider deep learning models trained to classify the synthesis conditions of uranium ore concentrates (UOCs) and show that model editing is particularly effective for improving generalization to distribution shifts common in this domain. In particular, model editing outperforms finetuning on two curated datasets comprising of micrographs taken of U$_{3}$O$_{8}$ aged in humidity chambers and micrographs acquired with different scanning electron microscopes, respectively.<|reference_end|>
@article{brown2024model,
    title={Model editing for distribution shifts in uranium oxide morphological
  analysis},
    author={Davis Brown, Cody Nizinski, Madelyn Shapiro, Corey Fallon, Tianzhixi
  Yin, Henry Kvinge, and Jonathan H. Tu},
    journal={arXiv preprint arXiv:2407.15756},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15756},
    primaryClass={cs.LG cs.AI}
}