Tag: MLCMU

Beyond the Mud: Datasets, Benchmarks, and Methods for Computer Vision in Off-Road Racing – Machine Learning Blog | ML@CMU

TL;DR: Off-the-shelf text spotting and re-identification models fail in basic off-road racing settings, even more so during muddy events. Making

TLDR; Design biases in NLP systems, such as performance differences for different populations, often stem from their creator’s positionality, i.e.,

Evaluating models in federated networks is challenging due to factors such as client subsampling, data heterogeneity, and privacy. These factors