Okładka

Model editing for distribution shifts in uranium oxide morphological analysis

Zapisane w:
Wydane w:arXiv.org (Jul 22, 2024), p. n/a
1. autor: Brown, Davis
Kolejni autorzy: Nizinski, Cody, Shapiro, Madelyn, Fallon, Corey, Yin, Tianzhixi, Kvinge, Henry, Tu, Jonathan H
Wydane:
Cornell University Library, arXiv.org
Hasła przedmiotowe:
Uranium ores
Editing
Photomicrographs
Deep learning
Measuring instruments
Uranium oxides
Dostęp online:Citation/Abstract
Full text outside of ProQuest
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022 |a 2331-8422 
035 |a 3083763541 
045 0 |b d20240722 
100 1 |a Brown, Davis 
245 1 |a Model editing for distribution shifts in uranium oxide morphological analysis 
260 |b Cornell University Library, arXiv.org  |c Jul 22, 2024 
513 |a Working Paper 
520 3 |a 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. 
653 |a Uranium ores 
653 |a Editing 
653 |a Photomicrographs 
653 |a Deep learning 
653 |a Measuring instruments 
653 |a Uranium oxides 
700 1 |a Nizinski, Cody 
700 1 |a Shapiro, Madelyn 
700 1 |a Fallon, Corey 
700 1 |a Yin, Tianzhixi 
700 1 |a Kvinge, Henry 
700 1 |a Tu, Jonathan H 
773 0 |t arXiv.org  |g (Jul 22, 2024), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3083763541/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/2407.15756