# Model editing for distribution shifts in uranium oxide morphological analysis

arXiv: https://arxiv.org/abs/2407.15756 · HTML: https://mlsanity.com/paper/2407.15756/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 34 references that are in the map.
- First posted on arXiv 22 Jul 2024.

## Builds on (most cited first, 11 of 11)

- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Locating and Editing Factual Associations in GPT](https://mlsanity.com/paper/2202.05262/) (2022)
- [Editing Models with Task Arithmetic](https://mlsanity.com/paper/2212.04089/) (2022)
- [Fast Model Editing at Scale](https://mlsanity.com/paper/2110.11309/) (2021)
- [Surgical Fine-Tuning Improves Adaptation to Distribution Shifts](https://mlsanity.com/paper/2210.11466/) (2022)
- [Rewriting a Deep Generative Model](https://mlsanity.com/paper/2007.15646/) (2020)
- [Editing a classifier by rewriting its prediction rules](https://mlsanity.com/paper/2112.01008/) (2021)
- [Discovering Bugs in Vision Models using Off-the-shelf Image Generation and Captioning](https://mlsanity.com/paper/2208.08831/) (2022)
- [Identifying and Mitigating Model Failures through Few-shot CLIP-aided Diffusion Generation](https://mlsanity.com/paper/2312.05464/) (2023)
- [DBCal: Density Based Calibration of classifier predictions for uncertainty quantification](https://mlsanity.com/paper/2204.00150/) (2022)
- [Improving Data-Efficient Fossil Segmentation via Model Editing](https://mlsanity.com/paper/2210.03879/) (2022)

## Abstract (arXiv)

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 U3O8 aged in humidity chambers and micrographs acquired with different scanning electron microscopes, respectively.