Deep Learning is the most widely used tool in the contemporary field of computer vision. Its
ability to accurately solve complex problems is employed in vision research to learn deep …

Machine learning (ML) provides effective means to learn from spectrum data and solve
complex tasks involved in wireless communications. Supported by recent advances in …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Deep learning is at the heart of the current rise of artificial intelligence. In the field of
computer vision, it has become the workhorse for applications ranging from self-driving cars …

Deep neural networks (DNNs) have been widely applied to various applications, including
image classification, text generation, audio recognition, and graph data analysis. However …

Federated machine unlearning (FMU) aims to remove the influence of a specified subset of
training data upon request from a trained federated learning model. Despite achieving …

The right to be forgotten calls for efficient machine unlearning techniques that make trained
machine learning models forget a cohort of data. The combination of training and unlearning …

Abstract Machine learning based traffic forecasting models leverage sophisticated
spatiotemporal auto-correlations to provide accurate predictions of city-wide traffic states …

While the celebrated graph neural networks (GNNs) yield effective representations for
individual nodes of a graph, there has been relatively less success in extending to the task …

Recent findings have shown multiple graph learning models, such as graph classification
and graph matching, are highly vulnerable to adversarial attacks, ie small input …