ABSTRACT A common need for artificial intelligence models in the broader geoscience is to
encode various types of spatial data, such as points, polylines, polygons, graphs, or rasters …

Recommender systems are one of the most successful applications of data mining and
machine-learning technology in practice. Academic research in the field is historically often …

Large knowledge graphs often grow to store temporal facts that model the dynamic relations
or interactions of entities along the timeline. Since such temporal knowledge graphs often …

We introduce a new family of deep neural network models. Instead of specifying a discrete
sequence of hidden layers, we parameterize the derivative of the hidden state using a …

Modeling sequential interactions between users and items/products is crucial in domains
such as e-commerce, social networking, and education. Representation learning presents …

Representation Learning over graph structured data has received significant attention
recently due to its ubiquitous applicability. However, most advancements have been made …

Graphs arise naturally in many real-world applications including social networks,
recommender systems, ontologies, biology, and computational finance. Traditionally …

Time is an important feature in many applications involving events that occur synchronously
and/or asynchronously. To effectively consume time information, recent studies have …

Human mobility prediction is of great importance for a wide spectrum of location-based
applications. However, predicting mobility is not trivial because of three challenges: 1) the …

Modern data acquisition routinely produce massive amounts of event sequence data in
various domains, such as social media, healthcare, and financial markets. These data often …