We propose a two-sample testing procedure based on learned deep neural network
representations. To this end, we define two test statistics that perform an asymptotic location …

When sample sizes are small, the ability to identify weak (but scientifically interesting)
associations between a set of predictors and a response may be enhanced by pooling …

Learning from very few samples is a challenge for machine learning tasks, such as text and
image classification. Performance of such task can be enhanced via transfer of helpful …

We develop a conditional generative model for longitudinal image datasets based on
sequential invertible neural networks. Longitudinal image acquisitions are common in …

Many studies in biomedical and health sciences involve small sample sizes due to logistic or
financial constraints. Often, identifying weak (but scientifically interesting) associations …

Prompt Tuning has emerged as a prominent research paradigm for adapting vision-
language models to various downstream tasks. However, recent research indicates that …

Comparing and testing for the homogeneity of two independent random samples is a
fundamental statistical problem with many applications across various fields. However …

An important question in many machine learning applications is whether two samples arise
from the same generating distribution. Although an old topic in Statistics, simple …

Improving autism spectrum disorder (ASD) diagnosis through effective utilization of multi-
center data has attracted increasing attention recently. However, most previous studies do …

Cross-media retrieval, which uses a text query to search for images and vice-versa, has
attracted a wide attention in recent years. The mostly existing cross-media retrieval methods …