3 papers
cs.CV2025
CHAMMI-75: Pre-training multi-channel models with heterogeneous microscopy images
Vidit Agrawal, John Peters, Tyler N. Thompson +13
Quantifying cell morphology using images and machine learning has proven to be a powerful tool to study the response of cells to treatments. However, models used to quantify cellul…
cs.CV2025
Predicting Performance of Object Detection Models in Electron Microscopy Using Random Forests
Ni Li, Ryan Jacobs, Matthew Lynch +3
Quantifying prediction uncertainty when applying object detection models to new, unlabeled datasets is critical in applied machine learning. This study introduces an approach to es…
cs.LG2024
Accelerating Ensemble Error Bar Prediction with Single Models Fits
Vidit Agrawal, Shixin Zhang, Lane E. Schultz +1
Ensemble models can be used to estimate prediction uncertainties in machine learning models. However, an ensemble of N models is approximately N times more computationally demandin…