The data-science revolution is poised to transform the way photonic systems are simulated
and designed. Photonic systems are, in many ways, an ideal substrate for machine learning …

With the down-scaling of CMOS technology, the design complexity of very large-scale
integrated is increasing. Although the application of machine learning (ML) techniques 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 …

The rapid evolution of Graph Neural Networks (GNNs) has led to a growing number of new
architectures as well as novel applications. However, current research focuses on proposing …

Deep learning on graphs has become one of the hottest topics in machine learning. The
book consists of four parts to best accommodate our readers with diverse backgrounds and …

This paper studies unsupervised/self-supervised whole-graph representation learning,
which is critical in many tasks such as molecule properties prediction in drug and material …

Automatic transistor sizing is a challenging problem in circuit design due to the large design
space, complex performance tradeoffs, and fast technology advancements. Although there …

In the last decade, deep learning has reinvigorated the machine learning field. It has solved
many problems in computer vision, speech recognition, natural language processing, and …

Graph neural networks (GNNs) are a type of deep learning models that are trained on
graphs and have been successfully applied in various domains. Despite the effectiveness of …

It has been a long time that computer architecture and systems are optimized for efficient
execution of machine learning (ML) models. Now, it is time to reconsider the relationship …