Although effective deepfake detection models have been developed in recent years recent
studies have revealed that these models can result in unfair performance disparities among …

Despite the development of effective deepfake detectors in recent years, recent studies have
demonstrated that biases in the data used to train these detectors can lead to disparities in …

In the realm of medical imaging, particularly for COVID-19 detection, deep learning models
face substantial challenges such as the necessity for extensive computational resources, the …

Human affective behavior analysis aims to delve into human expressions and behaviors to
deepen our understanding of human emotions. Basic expression categories (EXPR) and …

Supervised learning models are challenged by the intrinsic complexities of training data
such as outliers and minority subpopulations and intentional attacks at inference time with …

Objective To compare the performance of three machine learning algorithms with the tumor,
node, and metastasis (TNM) staging system in survival prediction and validate the individual …

Survival analysis is a general framework for predicting the time until a specific event occurs,
often in the presence of censoring. Although this framework is widely used in practice, few …

Diffusion models (DMs) have revolutionized image generation, producing high-quality
images with applications spanning various fields. However, their ability to create hyper …

Survival analysis is a general framework for predicting the time until a specific event occurs,
often in the presence of censoring. Although this framework is widely used in practice, few …

As machine learning (ML) systems are becoming pervasive in high-stakes applications, the
issue of ML fairness is receiving increasing attention. A large variety of fair ML solutions …