The popularity of deep learning has led to the curation of a vast number of massive and
multifarious datasets. Despite having close-to-human performance on individual tasks …

Methods for carefully selecting or generating a small set of training data to learn from, ie,
data pruning, coreset selection, and data distillation, have been shown to be effective in …

Neural networks are powerful machine learning models, but their reliability and trust are
often criticized due to the unclear nature of their internal learned relationships. We explored …

We introduce MODEL SELECTOR, a framework for label-efficient selection of pretrained
classifiers. Given a pool of unlabeled target data, MODEL SELECTOR samples a small …

In the era of exceptionally data-hungry models, careful selection of the training data is
essential to mitigate the extensive costs of deep learning. Data pruning offers a solution by …

The amount of data available to train modern machine learning systems has been
increasing rapidly, so much so that we're using, eg, entirety of the publicly available text data …

Deep learning has shown great success in several areas, including speech recognition,
natural language processing, and computer vision, but its effectiveness significantly …