Learn the distinctions between simple and stratified random sampling. Understand how researchers use these methods to accurately represent data populations.

Behavioral economics relies heavily on studies of Western, educated people. A recent analysis provides evidence that ignoring racial diversity within the United States has led to flawed ...

Welcome to the repository for our paper: "Rethinking Domain Generalization: Discriminability and Generalizability." You can use the following training command to train DMDA. We provide the sample on ...

This important study describes long-range serial dependence of performance on a visual texture discrimination training task that manipulated conditions to induce differing degrees of location transfer ...

Abstract: Deep neural networks are vulnerable to universal adversarial perturbation (UAP), an instance-agnostic perturbation capable of fooling the target model for most samples. Compared to ...

It’s a familiar moment in math class—students are asked to solve a problem, and some jump in confidently while others freeze, unsure where to begin. When students don’t yet have a clear mental model ...

Abstract: In this paper, we propose an Aggregation and Separation Domain Generalization (ASDG) method for Audio DeepFake Detection (ADD). Fake speech generated from different methods exhibits varied ...

Invariant graph representation learning aims to learn the invariance among data from different environments for out-of-distribution generalization on graphs. As the graph environment partitions are ...