Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment
and accelerate research, hel** scientists to generate hypotheses, design experiments …

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 …

Multimodal datasets are a critical component in recent breakthroughs such as CLIP, Stable
Diffusion and GPT-4, yet their design does not receive the same research attention as model …

Large language models (LLMs) transfer well to new tasks out-of-the-box simply given a
natural language prompt that demonstrates how to perform the task and no additional …

We identify label errors in the test sets of 10 of the most commonly-used computer vision,
natural language, and audio datasets, and subsequently study the potential for these label …

Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler
of its great success is the availability of abundant and high-quality data for building machine …

Learning exists in the context of data, yet notions of confidence typically focus on model
predictions, not label quality. Confident learning (CL) is an alternative approach which …

Supervised training of deep learning models requires large labeled datasets. There is a
growing interest in obtaining such datasets for medical image analysis applications …

In recent years, deep learning has garnered tremendous success in a variety of application
domains. This new field of machine learning has been growing rapidly and has been …

Data collection is a major bottleneck in machine learning and an active research topic in
multiple communities. There are largely two reasons data collection has recently become a …