Abstract Generative Adversarial Networks (GANs) can generate near photo realistic images
in narrow domains such as human faces. Yet, modeling complex distributions of datasets …

We study semi-supervised learning (SSL) for vision transformers (ViT), an under-explored
topic despite the wide adoption of the ViT architectures to different tasks. To tackle this …

Generative Adversarial Networks (GANs) are one of the most popular tools for learning
complex high dimensional distributions. However, generalization properties of GANs have …

Generative models have shown breakthroughs in a wide spectrum of domains due to recent
advancements in machine learning algorithms and increased computational power. Despite …

Semi-supervised learning (SSL) has long been proved to be an effective technique to
construct powerful models with limited labels. In the existing literature, consistency …

Graph Neural Networks (GNNs) have achieved promising performance in semi-supervised
node classification in recent years. However, the problem of insufficient supervision …

For the past decades, simulation‐based likelihood‐free inference methods have enabled
researchers to address numerous population genetics problems. As the richness and …

Metal artifact reduction (MAR) is one of the most important research topics in computed
tomography (CT). With the advance of deep learning approaches for image reconstruction …

Generative models have proven to be an outstanding tool for representing high-dimensional
probability distributions and generating realistic looking images. An essential characteristic …

Graph classification has recently received a lot of attention from various fields of machine
learning eg kernel methods, sequential modeling or graph embedding. All these …