As artificial intelligence (AI) transitions from research to deployment, creating the appropriate
datasets and data pipelines to develop and evaluate AI models is increasingly the biggest …

The leading approaches in Machine Learning are notoriously data-hungry. Unfortunately,
many application domains do not have access to big data because acquiring data involves a …

Data-centric AI is at the center of a fundamental shift in software engineering where machine
learning becomes the new software, powered by big data and computing infrastructure …

To help researchers conduct a systematic review or meta-analysis as efficiently and
transparently as possible, we designed a tool to accelerate the step of screening titles and …

In real-world classification tasks, each class often comprises multiple finer-grained"
subclasses." As the subclass labels are frequently unavailable, models trained using only …

As big data becomes ubiquitous across domains, and more and more stakeholders aspire to
make the most of their data, demand for machine learning tools has spurred researchers to …

The extraction of labels from radiology text reports enables large-scale training of medical
imaging models. Existing approaches to report labeling typically rely either on sophisticated …

Fine-tuned pre-trained language models (LMs) have achieved enormous success in many
natural language processing (NLP) tasks, but they still require excessive labeled data in the …

Named Entity Recognition (NER) performance often degrades rapidly when applied to target
domains that differ from the texts observed during training. When in-domain labelled data is …

Ground-truth labeling is an important activity in machine learning. Many studies have
examined how crowdworkers apply labels to records in machine learning datasets …