Graph is a natural data structure for describing complex systems, which contains a set of
objects and relationships. Ubiquitous real-life biomedical problems can be modeled as …

Spiking neural networks (SNNs) are inspired by information processing in biology, where
sparse and asynchronous binary signals are communicated and processed in a massively …

Synthesizing photo‐realistic images and videos is at the heart of computer graphics and has
been the focus of decades of research. Traditionally, synthetic images of a scene are …

RNA molecules adopt three-dimensional structures that are critical to their function and of
interest in drug discovery. Few RNA structures are known, however, and predicting them …

Federated learning (FL) enables edge devices, such as Internet of Things devices (eg,
sensors), servers, and institutions (eg, hospitals), to collaboratively train a machine learning …

The COVID-19 pandemic is causing a major outbreak in more than 150 countries around the
world, having a severe impact on the health and life of many people globally. One of the …

Accurate traffic flow prediction is valuable for satisfying citizens' travel needs and alleviating
urban traffic pressure. However, it is highly challenging due to the complexity of the urban …

We provide practical and powerful schemes for learning properties of a quantum state using
a small number of measurements. Specifically, we present a randomized measurement …

Deep learning has profoundly impacted many areas of machine learning. However, it took a
while for its impact to be felt in the field of recommender systems. In this article, we outline …

Machine learning has played an important role in the analysis of high-energy physics data
for decades. The emergence of deep learning in 2012 allowed for machine learning tools …