no code implementations • 16 Jan 2024 • Abhijith Gandrakota, Lily Zhang, Aahlad Puli, Kyle Cranmer, Jennifer Ngadiuba, Rajesh Ranganath, Nhan Tran
Anomaly, or out-of-distribution, detection is a promising tool for aiding discoveries of new particles or processes in particle physics.
no code implementations • 24 Aug 2023 • Aahlad Puli, Lily Zhang, Yoav Wald, Rajesh Ranganath
However, even when the stable feature determines the label in the training distribution and the shortcut does not provide any additional information, like in perception tasks, default-ERM still exhibits shortcut learning.
1 code implementation • 8 Aug 2023 • Rhys Compton, Lily Zhang, Aahlad Puli, Rajesh Ranganath
In machine learning, incorporating more data is often seen as a reliable strategy for improving model performance; this work challenges that notion by demonstrating that the addition of external datasets in many cases can hurt the resulting model's performance.
1 code implementation • 16 May 2021 • Benjamin Townsend, Eamon Ito-Fisher, Lily Zhang, Madison May
Typically, information extraction (IE) requires a pipeline approach: first, a sequence labeling model is trained on manually annotated documents to extract relevant spans; then, when a new document arrives, a model predicts spans which are then post-processed and standardized to convert the information into a database entry.