most citedEdit at your own risk: evaluating the robustness of edited models to distribution shifts

1 citations · 1 across the 2 of their papers we have counts for

collaborators

2 papers

cs.LG2024

Model editing for distribution shifts in uranium oxide morphological analysis

Davis Brown, Cody Nizinski, Madelyn Shapiro +4

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 th…

cs.LG2023★ 1 cited

Edit at your own risk: evaluating the robustness of edited models to distribution shifts

Davis Brown, Charles Godfrey, Cody Nizinski +2

The current trend toward ever-larger models makes standard retraining procedures an ever-more expensive burden. For this reason, there is growing interest in model editing, which e…