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

This paper examines the problems of severe image-text misalignment and high redundancy
in the widely-used large-scale Vision-Language Pre-Training (VLP) datasets. To address …

Modern deep learning systems require huge data sets to achieve impressive performance,
but there is little guidance on how much or what kind of data to collect. Over-collecting data …

Traditionally, data selection has been studied in settings where all samples from prospective
sources are fully revealed to a machine learning developer. However, in practical data …

When machine learning is outsourced to a rational agent, conflicts of interest might arise and
severely impact predictive performance. In this work, we propose a theoretical framework for …

In this work, we share three insights for achieving state-of-the-art aesthetic quality in text-to-
image generative models. We focus on three critical aspects for model improvement …

Annotating new datasets for machine learning tasks is tedious, time-consuming, and costly.
For segmentation applications, the burden is particularly high as manual delineations of …

We propose an approach to estimate the number of samples required for a model to reach a
target performance. We find that the power law, the de facto principle to estimate model …

Featured Application Our machine learning-based label quality assurance demo showcases
the potential of our approach to improve object detection projects within the data …

Wing design optimization traditionally involves computationally expensive high-fidelity
simulations, limiting the exploration of design spaces. In this study, we propose a …